The document describes 12 programs related to neural networks and fuzzy logic. Program 1 performs set operations on matrices. Program 2 implements De Morgan's laws. Program 3 plots various membership functions. Programs 4-5 implement fuzzy inference systems to model tip amounts. Programs 6-7 generate AND/ANDNOT and XOR functions using McCulloch-Pitts neurons. Programs 8-10 involve Hebb nets, perceptrons, and hetero-associative nets. Programs 11-12 involve auto-associative and Hopfield nets to store and recall patterns.
The slide covers the basic concepts and designs of artificial neural networks. It explains and justifies the use of McCulloh Pitts Model, Adaline network, Perceptron algorithm, Backpropagation algorithm, Hopfield network and Kohonen network; along with its practical applications.
This presentation discusses about the following topics:
Truth values and tables,
Fuzzy propositions,
Formation of rules decomposition of rules,
Aggregation of fuzzy rules,
Fuzzy reasoning‐fuzzy inference systems
Overview of fuzzy expert system‐
Fuzzy decision making.
The document discusses artificial neural networks and backpropagation. It provides an overview of backpropagation algorithms, including how they were developed over time, the basic methodology of propagating errors backwards, and typical network architectures. It also gives examples of applying backpropagation to problems like robotics, space robots, handwritten digit recognition, and face recognition.
Fuzzy inference systems use fuzzy logic to map inputs to outputs. There are two main types:
Mamdani systems use fuzzy outputs and are well-suited for problems involving human expert knowledge. Sugeno systems have faster computation using linear or constant outputs.
The fuzzy inference process involves fuzzifying inputs, applying fuzzy logic operators, and using if-then rules. Outputs are determined through implication, aggregation, and defuzzification. Mamdani systems find the centroid of fuzzy outputs while Sugeno uses weighted averages, making it more efficient.
Computer Graphic - Lines, Circles and Ellipse2013901097
1. The document describes algorithms for drawing lines, circles, and ellipses using a midpoint technique. It provides examples showing the steps and calculations for applying each algorithm.
2. Key steps of the line drawing algorithm include calculating slope, change in x and y, and a decision parameter to determine the next point. Circles use a decision parameter comparing radius to x and y values. Ellipses use two regions and decision parameters involving radii and x/y values.
3. Examples are provided applying each algorithm to draw specific geometric shapes given endpoint or radius values. Tables show the calculations and plotted points at each iteration.
The document provides information about multi-layer perceptrons (MLPs) and backpropagation. It begins with definitions of perceptrons and MLP architecture. It then describes backpropagation, including the backpropagation training algorithm and cycle. Examples are provided, such as using an MLP to solve the exclusive OR (XOR) problem. Applications of backpropagation neural networks and options like momentum, batch vs sequential training, and adaptive learning rates are also discussed.
The document provides an introduction to artificial neural networks and their components. It discusses the basic neuron model, including the summation function, activation function, and bias. It also covers various neuron models based on different activation functions. The document introduces different network architectures, including single-layer feedforward networks, multilayer feedforward networks, and recurrent networks. It discusses perceptrons, ADALINE networks, and the backpropagation algorithm for training multilayer networks. The limitations of perceptrons for non-linearly separable problems are also covered.
Nural network ER. Abhishek k. upadhyay Learning rulesabhishek upadhyay
This document discusses several types of activation functions and learning rules used in neural networks. It describes unipolar and bipolar activation functions, and provides an example of a feedforward neural network using tanh and linear activation functions. It then summarizes Hebbian, perceptron, delta, Widrow-Hoff, correlation, winner-take-all, and outstar learning rules, explaining how each updates network weights based on different error or activation signals.
The slide covers the basic concepts and designs of artificial neural networks. It explains and justifies the use of McCulloh Pitts Model, Adaline network, Perceptron algorithm, Backpropagation algorithm, Hopfield network and Kohonen network; along with its practical applications.
This presentation discusses about the following topics:
Truth values and tables,
Fuzzy propositions,
Formation of rules decomposition of rules,
Aggregation of fuzzy rules,
Fuzzy reasoning‐fuzzy inference systems
Overview of fuzzy expert system‐
Fuzzy decision making.
The document discusses artificial neural networks and backpropagation. It provides an overview of backpropagation algorithms, including how they were developed over time, the basic methodology of propagating errors backwards, and typical network architectures. It also gives examples of applying backpropagation to problems like robotics, space robots, handwritten digit recognition, and face recognition.
Fuzzy inference systems use fuzzy logic to map inputs to outputs. There are two main types:
Mamdani systems use fuzzy outputs and are well-suited for problems involving human expert knowledge. Sugeno systems have faster computation using linear or constant outputs.
The fuzzy inference process involves fuzzifying inputs, applying fuzzy logic operators, and using if-then rules. Outputs are determined through implication, aggregation, and defuzzification. Mamdani systems find the centroid of fuzzy outputs while Sugeno uses weighted averages, making it more efficient.
Computer Graphic - Lines, Circles and Ellipse2013901097
1. The document describes algorithms for drawing lines, circles, and ellipses using a midpoint technique. It provides examples showing the steps and calculations for applying each algorithm.
2. Key steps of the line drawing algorithm include calculating slope, change in x and y, and a decision parameter to determine the next point. Circles use a decision parameter comparing radius to x and y values. Ellipses use two regions and decision parameters involving radii and x/y values.
3. Examples are provided applying each algorithm to draw specific geometric shapes given endpoint or radius values. Tables show the calculations and plotted points at each iteration.
The document provides information about multi-layer perceptrons (MLPs) and backpropagation. It begins with definitions of perceptrons and MLP architecture. It then describes backpropagation, including the backpropagation training algorithm and cycle. Examples are provided, such as using an MLP to solve the exclusive OR (XOR) problem. Applications of backpropagation neural networks and options like momentum, batch vs sequential training, and adaptive learning rates are also discussed.
The document provides an introduction to artificial neural networks and their components. It discusses the basic neuron model, including the summation function, activation function, and bias. It also covers various neuron models based on different activation functions. The document introduces different network architectures, including single-layer feedforward networks, multilayer feedforward networks, and recurrent networks. It discusses perceptrons, ADALINE networks, and the backpropagation algorithm for training multilayer networks. The limitations of perceptrons for non-linearly separable problems are also covered.
Nural network ER. Abhishek k. upadhyay Learning rulesabhishek upadhyay
This document discusses several types of activation functions and learning rules used in neural networks. It describes unipolar and bipolar activation functions, and provides an example of a feedforward neural network using tanh and linear activation functions. It then summarizes Hebbian, perceptron, delta, Widrow-Hoff, correlation, winner-take-all, and outstar learning rules, explaining how each updates network weights based on different error or activation signals.
Here is a MATLAB program to implement logic functions using a McCulloch-Pitts neuron:
% McCulloch-Pitts neuron for logic functions
% Inputs
x1 = 1;
x2 = 0;
% Weights
w1 = 1;
w2 = 1;
% Threshold
theta = 2;
% Net input
net = x1*w1 + x2*w2;
% Activation function
if net >= theta
y = 1;
else
y = 0;
end
% Output
disp(y)
This implements a basic AND logic gate using a McCulloch-Pitts neuron.
The MATLAB program defines 4 input patterns and their corresponding 2 target patterns for a hetero-associative neural network. It initializes the weights to 0 and calculates the weights by taking the sum of the input patterns multiplied by their respective target patterns. The final weight matrix and bias are displayed.
Artificial intelligence agents can be defined as entities that perceive their environment through sensors, and act upon the environment through effectors to achieve goals or perform tasks. The document discusses different types of agents including table-driven agents, reflex agents, agents with memory, goal-based agents, and utility-based agents. It also covers key concepts in agent design like the PEAS framework and properties of environments that agents operate in.
The document discusses various neural network learning rules:
1. Error correction learning rule (delta rule) adapts weights based on the error between the actual and desired output.
2. Memory-based learning stores all training examples and classifies new inputs based on similarity to nearby examples (e.g. k-nearest neighbors).
3. Hebbian learning increases weights of simultaneously active neuron connections and decreases others, allowing patterns to emerge from correlations in inputs over time.
4. Competitive learning (winner-take-all) adapts the weights of the neuron most active for a given input, allowing unsupervised clustering of similar inputs across neurons.
This document provides an overview of multilayer perceptrons (MLPs) and the backpropagation algorithm. It defines MLPs as neural networks with multiple hidden layers that can solve nonlinear problems. The backpropagation algorithm is introduced as a method for training MLPs by propagating error signals backward from the output to inner layers. Key steps include calculating the error at each neuron, determining the gradient to update weights, and using this to minimize overall network error through iterative weight adjustment.
The document discusses several shortest path algorithms for graphs, including Dijkstra's algorithm, Bellman-Ford algorithm, and Floyd-Warshall algorithm. Dijkstra's algorithm finds the shortest path from a single source node to all other nodes in a graph with non-negative edge weights. Bellman-Ford can handle graphs with negative edge weights but is slower. Floyd-Warshall can find shortest paths in a graph between all pairs of nodes.
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
The document discusses the Least-Mean Square (LMS) algorithm. It begins by introducing LMS as the first linear adaptive filtering algorithm developed by Widrow and Hoff in 1960. It then describes the filtering structure of LMS, modeling an unknown dynamic system using a linear neuron model and adjusting weights based on an error signal. Finally, it summarizes the LMS algorithm, outlines its virtues like computational simplicity and robustness, and notes its primary limitation is slow convergence for high-dimensional problems.
This document provides an introduction to neural networks, including their basic components and types. It discusses neurons, activation functions, different types of neural networks based on connection type, topology, and learning methods. It also covers applications of neural networks in areas like pattern recognition and control systems. Neural networks have advantages like the ability to learn from experience and handle incomplete information, but also disadvantages like the need for training and high processing times for large networks. In conclusion, neural networks can provide more human-like artificial intelligence by taking approximation and hard-coded reactions out of AI design, though they still require fine-tuning.
One of the main reasons for the popularity of Dijkstra's Algorithm is that it is one of the most important and useful algorithms available for generating (exact) optimal solutions to a large class of shortest path problems. The point being that this class of problems is extremely important theoretically, practically, as well as educationally.
This document summarizes artificial neural networks. It discusses how neural networks are composed of interconnected neurons that can learn complex behaviors through simple principles. Neural networks can be used for applications like pattern recognition, noise reduction, and prediction. The key components of neural networks are neurons, synapses, weights, thresholds, and activation functions. Neural networks offer advantages like adaptability and fault tolerance, though they are not exact and can be complex. Examples of neural network applications discussed include object trajectory learning, radiosity for virtual reality, speechreading, target detection and tracking, and robotics.
Huffman coding is a lossless data compression algorithm that assigns variable-length binary codes to characters based on their frequencies, with more common characters getting shorter codes. It builds a Huffman tree from the character frequencies where the root node has the total frequency and interior nodes branch left or right. To encode a message, it traverses the tree assigning 0s and 1s to the path taken. This simulation shows building the Huffman tree for a sample message and assigns codes to each character, compressing the data from 160 bits to 45 bits. Huffman coding has time complexity of O(n log n) and is commonly used in file compression, multimedia, and communication applications, providing efficient compression at the cost of slower encoding and
A production system is a type of artificial intelligence program that uses rules to represent knowledge and solve problems. It consists of productions, which are if-then statements that specify a condition and corresponding action. Productions execute to achieve a goal. Strong method production systems rely heavily on domain-specific knowledge, while weak method systems use general logic and reasoning techniques applicable to many problems without deep knowledge of any single domain.
The document discusses the sum of subsets problem, which involves finding all subsets of positive integers that sum to a given number. It describes the problem, provides an example, and explains that backtracking can be used to systematically consider subsets. A pruned state space tree is shown for a sample problem to illustrate the backtracking approach. An algorithm for the backtracking solution to the sum of subsets problem is presented.
Fuzzy and Neural Approaches in Engineering MATLABESCOM
This document provides an introduction to a MATLAB supplement for the book "Fuzzy and Neural Approaches in Engineering". It describes MATLAB as an educational software package for technical computing. The supplement contains MATLAB code examples that demonstrate concepts from the book, such as neural networks, fuzzy logic, and hybrid systems. It is intended to help readers gain a practical understanding of implementing soft computing techniques in MATLAB.
This document provides a brief list of MATLAB commands organized into sections on basic commands, plotting commands, equation fitting, data analysis, special matrices, matrix algebra, and solving simultaneous equations. Some key commands include matlab to load MATLAB, quit to exit, plot for plotting, polyfit for polynomial fitting, mean for calculating averages, and inv for solving equations using the matrix inverse.
Here is a MATLAB program to implement logic functions using a McCulloch-Pitts neuron:
% McCulloch-Pitts neuron for logic functions
% Inputs
x1 = 1;
x2 = 0;
% Weights
w1 = 1;
w2 = 1;
% Threshold
theta = 2;
% Net input
net = x1*w1 + x2*w2;
% Activation function
if net >= theta
y = 1;
else
y = 0;
end
% Output
disp(y)
This implements a basic AND logic gate using a McCulloch-Pitts neuron.
The MATLAB program defines 4 input patterns and their corresponding 2 target patterns for a hetero-associative neural network. It initializes the weights to 0 and calculates the weights by taking the sum of the input patterns multiplied by their respective target patterns. The final weight matrix and bias are displayed.
Artificial intelligence agents can be defined as entities that perceive their environment through sensors, and act upon the environment through effectors to achieve goals or perform tasks. The document discusses different types of agents including table-driven agents, reflex agents, agents with memory, goal-based agents, and utility-based agents. It also covers key concepts in agent design like the PEAS framework and properties of environments that agents operate in.
The document discusses various neural network learning rules:
1. Error correction learning rule (delta rule) adapts weights based on the error between the actual and desired output.
2. Memory-based learning stores all training examples and classifies new inputs based on similarity to nearby examples (e.g. k-nearest neighbors).
3. Hebbian learning increases weights of simultaneously active neuron connections and decreases others, allowing patterns to emerge from correlations in inputs over time.
4. Competitive learning (winner-take-all) adapts the weights of the neuron most active for a given input, allowing unsupervised clustering of similar inputs across neurons.
This document provides an overview of multilayer perceptrons (MLPs) and the backpropagation algorithm. It defines MLPs as neural networks with multiple hidden layers that can solve nonlinear problems. The backpropagation algorithm is introduced as a method for training MLPs by propagating error signals backward from the output to inner layers. Key steps include calculating the error at each neuron, determining the gradient to update weights, and using this to minimize overall network error through iterative weight adjustment.
The document discusses several shortest path algorithms for graphs, including Dijkstra's algorithm, Bellman-Ford algorithm, and Floyd-Warshall algorithm. Dijkstra's algorithm finds the shortest path from a single source node to all other nodes in a graph with non-negative edge weights. Bellman-Ford can handle graphs with negative edge weights but is slower. Floyd-Warshall can find shortest paths in a graph between all pairs of nodes.
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
The document discusses the Least-Mean Square (LMS) algorithm. It begins by introducing LMS as the first linear adaptive filtering algorithm developed by Widrow and Hoff in 1960. It then describes the filtering structure of LMS, modeling an unknown dynamic system using a linear neuron model and adjusting weights based on an error signal. Finally, it summarizes the LMS algorithm, outlines its virtues like computational simplicity and robustness, and notes its primary limitation is slow convergence for high-dimensional problems.
This document provides an introduction to neural networks, including their basic components and types. It discusses neurons, activation functions, different types of neural networks based on connection type, topology, and learning methods. It also covers applications of neural networks in areas like pattern recognition and control systems. Neural networks have advantages like the ability to learn from experience and handle incomplete information, but also disadvantages like the need for training and high processing times for large networks. In conclusion, neural networks can provide more human-like artificial intelligence by taking approximation and hard-coded reactions out of AI design, though they still require fine-tuning.
One of the main reasons for the popularity of Dijkstra's Algorithm is that it is one of the most important and useful algorithms available for generating (exact) optimal solutions to a large class of shortest path problems. The point being that this class of problems is extremely important theoretically, practically, as well as educationally.
This document summarizes artificial neural networks. It discusses how neural networks are composed of interconnected neurons that can learn complex behaviors through simple principles. Neural networks can be used for applications like pattern recognition, noise reduction, and prediction. The key components of neural networks are neurons, synapses, weights, thresholds, and activation functions. Neural networks offer advantages like adaptability and fault tolerance, though they are not exact and can be complex. Examples of neural network applications discussed include object trajectory learning, radiosity for virtual reality, speechreading, target detection and tracking, and robotics.
Huffman coding is a lossless data compression algorithm that assigns variable-length binary codes to characters based on their frequencies, with more common characters getting shorter codes. It builds a Huffman tree from the character frequencies where the root node has the total frequency and interior nodes branch left or right. To encode a message, it traverses the tree assigning 0s and 1s to the path taken. This simulation shows building the Huffman tree for a sample message and assigns codes to each character, compressing the data from 160 bits to 45 bits. Huffman coding has time complexity of O(n log n) and is commonly used in file compression, multimedia, and communication applications, providing efficient compression at the cost of slower encoding and
A production system is a type of artificial intelligence program that uses rules to represent knowledge and solve problems. It consists of productions, which are if-then statements that specify a condition and corresponding action. Productions execute to achieve a goal. Strong method production systems rely heavily on domain-specific knowledge, while weak method systems use general logic and reasoning techniques applicable to many problems without deep knowledge of any single domain.
The document discusses the sum of subsets problem, which involves finding all subsets of positive integers that sum to a given number. It describes the problem, provides an example, and explains that backtracking can be used to systematically consider subsets. A pruned state space tree is shown for a sample problem to illustrate the backtracking approach. An algorithm for the backtracking solution to the sum of subsets problem is presented.
Fuzzy and Neural Approaches in Engineering MATLABESCOM
This document provides an introduction to a MATLAB supplement for the book "Fuzzy and Neural Approaches in Engineering". It describes MATLAB as an educational software package for technical computing. The supplement contains MATLAB code examples that demonstrate concepts from the book, such as neural networks, fuzzy logic, and hybrid systems. It is intended to help readers gain a practical understanding of implementing soft computing techniques in MATLAB.
This document provides a brief list of MATLAB commands organized into sections on basic commands, plotting commands, equation fitting, data analysis, special matrices, matrix algebra, and solving simultaneous equations. Some key commands include matlab to load MATLAB, quit to exit, plot for plotting, polyfit for polynomial fitting, mean for calculating averages, and inv for solving equations using the matrix inverse.
This document provides an overview of computer networks and networking concepts. It discusses what a computer network is, why networks are used, what components make up a network, and what networks do to reliably transmit data. It also describes different types of networks including LANs, MANs, and WANs; various network topologies such as star, bus, ring, tree, and mesh; and different transmission media used in networks. The key details covered include the purpose and advantages and disadvantages of different network types, topologies, and transmission media.
Classical relations and fuzzy relationsBaran Kaynak
This document discusses classical and fuzzy relations. It begins by introducing relations and their importance in fields like engineering, science, and mathematics. It then contrasts classical/crisp relations with fuzzy relations. Classical relations have binary relatedness between elements, while fuzzy relations have degrees of relatedness on a continuum between completely related and not related. The document provides examples and explanations of crisp relations, fuzzy relations, Cartesian products, compositions, and equivalence/tolerance relations. It demonstrates these concepts with examples involving sets of cities and bacteria strains.
The document discusses different ways to implement threading in Java programs. It provides code examples to demonstrate creating threads by extending the Thread class and implementing the Runnable interface. The code examples show printing output from both the main thread and child threads to illustrate threading concepts. Socket programming and RMI examples are also provided with code to implement client-server applications using threads.
This document discusses classical sets and fuzzy sets. It defines classical sets as having distinct elements that are either fully included or excluded from the set. Fuzzy sets allow for gradual membership, with elements having degrees of membership between 0 and 1. Operations like union, intersection, and complement are defined for both classical and fuzzy sets, with fuzzy set operations accounting for degrees of membership. Properties of classical and fuzzy sets and relations are also covered, noting differences like fuzzy sets not following the law of excluded middle.
The document describes how to use MATLAB's Fuzzy Logic Toolbox to solve fuzzy logic problems. It begins with an introduction to fuzzy logic and an overview of the toolbox. It then uses the example of balancing an inverted pendulum on a cart to demonstrate the fuzzy inference system design process. This involves defining membership functions, rules, and using toolbox tools to simulate the fuzzy controller.
The document describes a presentation for a school management system created by Soumya Subhadarshi Behera. The presentation includes an introduction, motivation, and system development sections. It provides background on the need for a school management system to efficiently manage student, employee, academic and other administrative data. It then covers the goals and components involved in developing the software system, including using Visual Basic 6.0 for the front end and Oracle for the back end database.
This document provides an overview of the scope and features of a School Management System created by Eximius Infotech Pvt. Ltd. The system aims to optimize and manage all key processes within a school, including student registration, library management, timetables, transportation, fees collection, attendance tracking, communication tools, human resources, and financial accounting. It consists of several comprehensive modules that cover areas like student information, courses/syllabus management, inventory, canteen operations, and more. The system is designed to be fully web-based with role-based access and customized dashboards for different user types like administrators, teachers, students and parents.
Este documento resume las características de varios tipos de redes neuronales artificiales como Adaline, Hopfield y Kohonen. Adaline es una red neuronal de un solo neuronio de salida que utiliza aprendizaje supervisado. La red Hopfield funciona como una memoria asociativa mediante aprendizaje no supervisado. Los mapas organizativos de Kohonen utilizan aprendizaje competitivo para mapear características de entrada. Estas redes tienen aplicaciones en reconocimiento de patrones, procesamiento de señales e imágenes.
The document discusses various types of Hebbian learning including:
1) Unsupervised Hebbian learning where weights are strengthened based on actual neural responses to stimuli without a target output.
2) Supervised Hebbian learning where weights are strengthened based on the desired neural response rather than the actual response to better approximate a target output.
3) Recognition networks like the instar rule which only updates weights when a neuron's output is active to recognize specific input patterns.
School admission process management system (Documention)Shital Kat
This document outlines the project plan for developing a School Admission Process Management System. It includes sections on project initiation and scheduling, diagrams of the system, a project cost estimation, designing the user interface, and plans for testing. The system will automate the currently manual paper-based admission process to make it faster and easier to use. It will store and process student personal, academic, and fee information using a web interface and backend database. Testing will include white box, black box, unit, integration, and system testing to ensure quality.
This document describes a student management system (SMS) developed as an extension to the Hospital Management Information System (HMIS) to manage student records for dental students across government hospitals in Gujarat. The SMS allows for management of admission, fees payment, exam scheduling, result entry and generation of reports. It follows an iterative development approach and uses a multilayer architecture with layers for data, control, business and presentation. Various diagrams like use case, class, entity-relationship and data flow are provided to depict the system. Screenshots demonstrate modules for admission, fees, exam scheduling and results. The system aims to reduce paper work and efficiently manage student information and resources.
Software Engineering Project On School Management System. its Presentation .Data flow diagram , use case diagram of SMS , class diagram of school management system , functional and non-functional requirements
Download completer BS Computer Science Degree Study Data
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PID Tuning using Ziegler Nicholas - MATLAB ApproachWaleed El-Badry
This is an unreleased lab for undergraduate Mechatronics students to know how to practice Ziegler Nicholas method to find the PID factors using MATLAB.
This is my second version of the quantum notes collected as part of my study.
This organizes content from various open source for study and reference only.
curve fitting or regression analysis-1.pptxabelmeketa
This document discusses curve fitting and regression analysis in MATLAB. It provides examples of using the polyfit function to find the best linear, exponential, power, and cubic fits to sample data sets. The polyfit function uses the least squares method to determine the coefficients of the best-fit polynomial curve to the data. Plots are shown comparing experimental data to the fitted curves.
This document summarizes and implements an ordinary differential equation (ODE) neural network using the Diffeqflux.jl library. It begins with an introduction to deep learning and neural networks. It then provides the mathematics behind modeling a simple multi-layer perceptron neural network as a system of ODEs. This includes derivations of the forward and backward propagation algorithms. Finally, it describes implementing a simple example ODE neural network using Diffeqflux.jl to demonstrate the approach.
This document provides instructions for using SNMP to monitor and manage a Didactum remote monitoring system. It describes the available MIB tables for monitoring elements like analog sensors, relays, dry contacts, traps, and logic. It also provides examples of how to view, set and change values using SNMP commands like snmptable, snmpset. Key aspects that can be monitored and managed include sensor readings, relay and outlet states, dry contact states, trap configurations, and logic rules.
The document discusses artificial neural networks and classification using backpropagation, describing neural networks as sets of connected input and output units where each connection has an associated weight. It explains backpropagation as a neural network learning algorithm that trains networks by adjusting weights to correctly predict the class label of input data, and how multi-layer feed-forward neural networks can be used for classification by propagating inputs through hidden layers to generate outputs.
This is my personal notes related to quantum computing, collected as part of my study. It offers quantum circuits, quantum algorithms, matrix operations, Kronecker, dot products, derivation of Pauli's X, Y , Z gates , preparation of Bell state using Hadamard and CNOT, and finally defines the six quantum states for a qubit
Switch Control and Time Delay
1. LEDs and switches
2. Keypad and LEDs
3. Keypad and 8-segment LED C language and Assembly Code for Freescale MC9S08AW60
The document provides an overview of quantum computing, including its history, data representation using qubits, quantum gates and operations, and Shor's algorithm for integer factorization. Shor's algorithm uses quantum parallelism and the quantum Fourier transform to find the period of a function, from which the factors of a number can be determined. While quantum computing holds promise for certain applications, classical computers will still be needed and future computers may be a hybrid of classical and quantum components.
Beginning direct3d gameprogramming10_shaderdetail_20160506_jintaeksJinTaek Seo
This document provides instructions for implementing normal mapping in 5 steps of a Direct3D game programming tutorial. Step 5 adds normal mapping by including a normal map texture, transforming light and eye vectors to tangent space, and modifying the pixel shader to sample the normal map and calculate lighting in tangent space. The client code is also updated to include the normal map texture and related variables.
In this paper a novel intelligent soft computing based cryptographic technique based on synchronization of
two chaotic systems (CSCT) between sender and receiver has been proposed to generate session key using
Pecora and Caroll (PC) method. Chaotic system has some unique features like sensitive to initial
conditions, topologically mixing; and dense periodic orbits. By nature, the Lorenz system is very sensitive
to initial conditions meaning that the error between attacker and receiver is going to grow exponentially if
there is a very slight difference between their initial conditions. All these features make chaotic system as
good alternatives for session key generation. In the proposed CSCT few parameters ( , b , r , x1 ,y2 and z2 )
are being exchanged between sender and receiver. Some of the parameter which takes major roles to form
the session key does not get transmitted via public channel, sender keeps these parameters secret. This way
of handling parameter passing mechanism prevents any kind of attacks during exchange of parameters like
sniffing, spoofing or phishing.
The document describes multilayer neural networks and their use for classification problems. It discusses how neural networks can handle continuous-valued inputs and outputs unlike decision trees. Neural networks are inherently parallel and can be sped up through parallelization techniques. The document then provides details on the basic components of neural networks, including neurons, weights, biases, and activation functions. It also describes common network architectures like feedforward networks and discusses backpropagation for training networks.
This document provides MATLAB examples of neural networks, including:
1. Calculating the output of a simple neuron and plotting it over a range of inputs.
2. Creating a custom neural network, defining its topology and transfer functions, training it on sample data, and calculating outputs.
3. Classifying linearly separable data with a perceptron network and plotting the decision boundary.
This document provides MATLAB examples of neural networks, including:
1. Calculating the output of a simple neuron and plotting it over a range of inputs.
2. Creating a custom neural network, defining its topology and transfer functions, training it on sample data, and calculating outputs.
3. Classifying linearly separable data with a perceptron network and plotting the decision boundary.
The document provides an introduction and overview of the Network Simulator 2 (NS2). It outlines the components and basic requirements of NS2, describes how to install and set up a simple wireless network simulation involving 2 nodes, and explains how to run the simulation script. The simulation will generate a trace file that can be analyzed to test wireless routing and mobility protocols.
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This document summarizes selection and looping concepts in programming. It discusses if-else statements for program control based on conditions, relational and logical operators to write logical expressions, and for loops to repeatedly execute statements. Examples are provided to illustrate converting algorithms to programs for tasks like finding the maximum of two numbers, ordering numbers, calculating sums and factorials using for loops.
This document describes a laboratory exercise involving the use of timers and real-time clocks implemented on an Altera DE0 board. It involves designing and implementing four circuits: 1) a modulo-k counter, 2) a 3-digit BCD counter, 3) a real-time clock displaying minutes and seconds, and 4) a circuit displaying Morse code representations of letters using LEDs. VHDL is used to describe the circuits, which are then compiled, simulated, and downloaded to the DE0 board for testing. Preparation includes writing and simulating VHDL code for parts I-III.
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Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
The document contains details about experiments performed in a Digital Signal Processing practical course. It includes the aims, apparatus required, theory, source code and results for experiments involving MATLAB programs to generate basic signals like impulse, step, ramp and exponential signals; sine and cosine signals; quantization; sampling theorem; linear convolution; autocorrelation; and cross-correlation. Programs were written in MATLAB to perform the various digital signal processing tasks and the output was verified.
The document/view architecture divides a program into four main classes: the document class stores the program's data, the view class handles displaying data and user interaction, the frame class contains UI elements like menus and toolbars, and the application class starts the program and handles Windows interaction. Documents represent the data, views provide interfaces to interact with documents, and the frame and application classes manage the overall application. This architecture provides reusable code, separates program responsibilities, and allows flexible user interfaces.
The document discusses Synchronous Optical Networking (SONET) and Synchronous Digital Hierarchy (SDH), which are standardized protocols for transmitting multiple digital signals over fiber optic cables. They were developed to replace older asynchronous systems and allow synchronized transport of data from different sources. Key features include high transmission rates up to 40Gbps, simple addition and removal of low-rate channels, high reliability through automatic backup mechanisms, and future compatibility with new services. The main differences between SONET and SDH are their standardized bit rates which were chosen to integrate existing network technologies.
This document provides VHDL code for implementing various logic gates and basic digital circuits. It includes code for AND, OR, NOT, NAND, NOR, XOR and XNOR gates. It also provides code for half adder, full adder, multiplexer, demultiplexer, decoder, encoder, comparator, BCD to binary converter, JK flip-flop, and an n-bit counter. For each circuit, the VHDL code and a sample waveform output is given. The purpose is to design these basic digital components using VHDL and simulate their behavior.
The document discusses four key topics:
1. It describes Local Area Networks (LANs), Wide Area Networks (WANs), and Metropolitan Area Networks (MANs), distinguishing their key characteristics such as physical size and ownership.
2. It outlines different network topologies - bus, ring, star, mesh - and their characteristics such as ease of implementation, cable requirements, and ability to handle disruptions.
3. It examines different types of transmission media - twisted pair copper wire, coaxial cable, fiber optics - and their properties like data rates and uses in local vs. long-distance networks.
4. It introduces several common network connection standards - RJ-45, BNC,
The document provides an index and descriptions of various topics related to web development including:
1. The modulus operator and examples of using it to check for divisibility.
2. Relational and logical operators like greater than, less than, equal to and examples of using them in code.
3. Descriptions of do-while and for loops with examples.
4. An example using a parameterized constructor to initialize cube dimensions.
5. Examples of string methods like startsWith, length, and trim.
6. Descriptions and examples of overloading methods and constructors.
7. An example of inheritance with overriding methods.
8. An interface example with animal classes
This document provides an index of 21 coding topics that include performing arithmetic operations, comparison of numbers, compound interest calculation, prime number checking, and palindrome checking. It also includes displaying a Fibonacci series, calculating simple interest, and swapping numbers without using three variables. The index provides the topic name and number for each item.
1. Sr. No. Program Remarks
1. To perform Union, Intersection and Complement
operations.
2. To implement De-Morgan’s Law.
3. To plot various membership functions.
4. To implement FIS Editor. Use Fuzzy toolbox to model tip
value that is given after a dinner based on quality ans
service.
5. To implement FIS Editor.
6. Generate ANDNOT function using McCulloch-Pitts
neural net.
7. Generate XOR function using McCulloch-Pitts neural
net.
8. Hebb Net to classify two dimensional input patterns in
bipolar with given targets.
9. Perceptron net for an AND function with bipolar inputs
and targets.
10. To calculate the weights for given patterns using hetero-
associative neural net.
11. To store vector in an auto-associative net. Find weight
matrix & test the net with input
12. To store the vector, find the weight matrix with no self-
connection. Test this using a discrete Hopfield net.
2. Program No. 1
Write a program in MATLAB to perform Union,Intersection and Complement operations.
%Enter Data
u=input('Enter First Matrix');
v=input('Enter Second Matrix');
%To Perform Operations
w=max(u,v);
p=min(u,v);
q1=1-u;
q2=1-v;
%Display Output
display('Union Of Two Matrices');
display(w);
display('Intersection Of Two Matrices');
display(p);
display('Complement Of First Matrix');
display(q1);
display('Complement Of Second Matrix');
display(q2);
Output:-
3. Enter First Matrix [0.3 0.4]
Enter Second Matrix [0.1 0.7]
Union Of Two Matrices
w =0.3000 0.7000
Intersection Of Two Matrices
p = 0.1000 0.4000
Complement Of First Matrix
q1 =0.7000 0.6000
Complement Of Second Matrix
q2 =0.9000 0.3000
4. Program No. 2
Write a program in MATLAB to implement De-Morgan’s Law.
De-Morgan’s Law c(i(u,v)) = max(c(u),c(v))
c(u(u,v)) = min(c(u),c(v))
%Enter Data
u=input('Enter First Matrix');
v=input('Enter Second Matrix');
%To Perform Operations
w=max(u,v);
p=min(u,v);
q1=1-u;
q2=1-v;
x1=1-w;
x2=min(q1,q2);
y1=1-p;
y2=max(q1,q2);
%Display Output
display('Union Of Two Matrices');
display(w);
display('Intersection Of Two Matrices');
display(p);
display('Complement Of First Matrix');
5. display(q1);
display('Complement Of Second Matrix');
display(q2);
display('De-Morgans Law');
display('LHS');
display(x1);
display('RHS');
display(x2);
display('LHS1');
display(y1);
display('RHS1');
display(y2);
Output:-
Enter First Matrix [0.3 0.4]
6. Enter Second Matrix [0.2 0.5]
Union Of Two Matrices
w =0.3000 0.5000
Intersection Of Two Matrices
p =0.2000 0.4000
Complement Of First Matrix
q1 =0.7000 0.6000
Complement Of Second Matrix
q2 =0.8000 0.5000
De-Morgans Law
LHS
x1 = 0.7000 0.5000
RHS
x2 = 0.7000 0.5000
LHS1
y1 =0.8000 0.6000
8. Program No. 3
Write a program in MATLAB to plot various membership functions.
%Triangular Membership Function
x=(0.0:1.0:10.0)';
y1=trimf(x, [1 3 5]);
subplot(311)
plot(x,[y1]);
%Trapezoidal Membership Function
x=(0.0:1.0:10.0)';
y1=trapmf(x, [1 3 5 7]);
subplot(312)
plot(x,[y1]);
%Bell-Shaped Membership Function
x=(0.0:0.2:10.0)';
y1=gbellmf(x, [1 2 5]);
subplot(313)
plot(x,[y1]);
Output:-
9.
10. Program No. 4
Use Fuzzy toolbox to model tip value that is given after a dinner which can be-not
good,satisfying,good and delightful and service which is poor,average or good and the tip
value will range from Rs. 10 to 100.
We are given the linguistic variables quality of food and sevice as input variables which can be
written as:
Quality(not good,satisfying,good,delightful)
Service(poor,average,good)
Similarly Output variable is Tip_value which may range from Rs. 10 to 100.
A Fuzzy system comprises the following modules:-
1. Fuzzification Interface
2. Fuzzy Inference Engine
3. Deffuzification Interface
Fuzzy sets are defined on each of the universe of discourse:-
Quality,service and tip value.
11. The values for Quality variable are selected for their respective ranges:-
Similarly values for Service variable are selected for their respective ranges :-
12. In general a compositional rule for inference involves the following procedure:=-
1. Compute memberships of current inputs in the relevant antecedent fuzzy set of rule.
13. 2. If the antecedents are in conjunctive form,the AND operation is replaced by a minimum,if OR
then by Maximum and similarly other operations are performed.
3. Scale or clip the consequent fuzzy set of the rule by a minimum value found in step 2 since
this gives the smallest degree to which the rule must fire.
4. Repeat steps 1-3 for each rule in the rule base.
Superpose the scaled or clipped consequent fuzzy sets formed by such a superposition.There
are numerous variants of the defuzzifications.
The output will be displayed as :-
14.
15.
16. Program No. 5
To implement FIS Editor.
FIS stands for Fuzzy Inference System.In FIS fuzzy rules are used for approximate reasoning.It
is the logical framework that allows us to design reasoning systems based on fuzzy set theory.
To illustrate these concepts we use example of Water Tank:-
FIS editor consists of following units:-
i) Input
ii) Inference System
iii) Output
The Water Level is considered as the Input variable and Valve status is taken as Output Variable.
17. The Input-Output Variable’s Membership functions should be plotted along with their ranges:-
The following screen appearance is obtained by clicking on the FIS Rule system indicator:-
Rules are added by selecting variable’s values and clicking on add rule menu each time a new
rule is added.
The fuzzy Rules defined for water tank are:-
IF level is ok,THEN there is no change in valve.
IF level is low,THEN valve is open in fast mode.
IF level is high,THEN valve is closed in fast mode.
18. The result is displayed as plots of input-output membership functions :-
Water Level(ok,low,high)
Valve Status(no change,open fast,closed fast)
The output in accordance with the input and rules provided by user is shown as(view-rule
viewer):-
21. Program No. 6
Generate ANDNOT function using McCulloch-Pitts neural net by MATLAB program.
%ANDNOT function using McCulloch-Pitts neuron
clear;
clc;
% Getting weights and threshold value
disp('Enter the weights');
w1=input('Weight w1=');
w2=input('Weight w2=');
disp('Enter threshold value');
theta=input('theta=');
y=[0 0 0 0];
x1=[0 0 1 1];
x2=[0 1 0 1];
z=[0 0 1 0];
con=1;
while con
zin = x1*w1+x2*w2;
for i=1:4
if zin(i)>=theta
y(i)=1;
else y(i)=0;
22. end
end
disp('Output of net=');
disp(y);
if y==z
con=0;
else
disp('Net is not learning Enter another set of weights and threshold value');
w1=input('Weight w1=');
w2=input('Weight w2=');
thete=input('theta=');
end
end
disp('McCulloch Pitts Net for ANDNOT function');
disp('Weights of neuron');
disp(w1);
disp(w2);
disp('Threshold value=');
disp(theta);
Output :-
Enter the weights
Weight w1=1
23. Weight w2=1
Enter threshold value
theta=1
Output of net= 0 1 1 1
Net is not learning Enter another set of weights and threshold value
Weight w1=1
Weight w2=-1
theta=1
Output of net=0 0 1 0
McCulloch Pitts Net for ANDNOT function
Weights of neuron
1
-1
Threshold value=
1
24. Program No. 7
Generate XOR function using McCulloch-Pitts neural net by MATLAB program.
% XOR function using McCulloch-Pitts neuron
clear;
clc;
% Getting weights and threshold value
disp('Enter the weights');
w11=input('Weight w11=');
w12=input('Weight w12=');
w21=input('Weight w21=');
w22=input('Weight w22=');
v1=input('Weight v1=');
v2=input('Weight v2=');
disp('Enter threshold value');
theta=input('theta=');
x1=[0 0 1 1];
x2=[0 1 0 1];
z=[0 1 1 0];
con=1;
while con
zin1 = x1*w11+x2*w21;
zin2 = x1*w21+x2*w22;
25. for i=1:4
if zin1(i)>=theta
y1(i)=1;
else y1(i)=0;
end
if zin2(i)>=theta
y2(i)=1;
else y2(i)=0;
end
end
yin=y1*v1+y2*v2;
for i=1:4
if yin(i)>=theta;
y(i)=1;
else
y(i)=0;
end
end
disp('Output of net=');
disp(y);
if y==z
con=0;
else
disp('Net is not learning Enter another set of weights and threshold value');
w11=input('Weight w11=');
26. w12=input('Weight w12=');
w21=input('Weight w21=');
w22=input('Weight w22=');
v1=input('Weight v1=');
v2=input('Weight v2=');
theta=input('theta=');
end
end
disp('McCulloch Pitts Net for XOR function');
disp('Weights of neuron Z1');
disp(w11);
disp(w21);
disp('Weights of neuron Z2');
disp(w12);
disp(w22);
disp('Weights of neuron Y');
disp(v1);
disp(v2);
disp('Threshold value=');
disp(theta);
Output :-
Enter the weights
Weight w11=1
Weight w12=-1
Weight w21=-1
27. Weight w22=1
Weight v1=1
Weight v2=1
Enter threshold value
theta=1
Output of net= 0 1 1 0
McCulloch Pitts Net for XOR function
Weights of neuron z1
1
-1
Weights of neuron z2
-1
1
Weights of neuron y
1
1
Threshold value= 1
28. Program No. 8
Write a MATLAB program for Hebb Net to classify two dimensional input patterns in
bipolar with their targets given below:
‘*’ indicates a ‘+’ and ‘.’ Indicates ‘-’
***** *****
*…. *….
***** *****
*…. *….
***** *
% Hebb Net to classify Two -Dimensional input patterns.
clear;
clc;
%Input Pattern
E=[1 1 1 1 1 -1 -1 -1 1 1 1 1 1 -1 -1 -1 1 1 1 1];
F=[1 1 1 1 1 -1 -1 -1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1];
X(1,1:20)=E;
X(2,1:20)=F;
w(1:20)=0;
t=[1 -1];
b=0;
for i=1:2
w=w+X(i,1:20)*t(i);
30. Program No. 9
Write a MATLAB program for Perceptron net for an AND function with bipolar inputs
and targets.
% Perceptron for AND Function
clear;
clc;
x=[1 1 -1 -1;1 -1 1 -1];
t=[1 -1 -1 -1];
w=[0 0];
b=0;
alpha=input('Enter Learning rate=');
theta=input('Enter Threshold Value=');
con=1;
epoch=0;
while con
con=0;
for i=1:4
yin=b+x(1,i)*w(1)+x(2,i)*w(2);
31. if yin>theta
y=1;
end
if yin<=theta & yin>=-theta
y=0;
end
if yin<-theta
y=-1;
end
if y-t(i)
con=1;
for j=1:2
w(j)=w(j)+alpha*t(i)*x(j,i);
end
b=b+alpha*t(i);
end
end
epoch=epoch+1;
end
disp('Perceptron for AND Function');
disp('Final Weight Matrix');
disp(w);
disp('Final Bias');
disp(b);
32. Output :-
Enter Learning rate=1
Enter Threshold Value=0.5
Perceptron for AND Function
Final Weight Matrix
1 1
Final Bias
-1
33. Program No. 10
Write a M-file to calculate the weights for the following patterns using hetero-associative
neural net for mapping four input vectors to two output vectors
S1 S2 S3 S4 t1 t2
1 1 0 0 1 0
1 0 1 0 1 0
1 1 1 0 0 1
0 1 1 0 0 1
% Hetero-associative neural net for mapping input vectors to output vectors.
clear;
clc;
x=[1 1 0 0;1 0 1 0;1 1 1 0;0 1 1 0];
t=[1 0;1 0;0 1;0 1];
w=zeros(4,2);
for i=1:4
w=w+x(i,1:4)'*t(i,1:2);
end
disp('Weight Matrix');
disp(w);
Output:-
35. Write an M-file to store vector[-1 -1 -1 -1] and [-1 -1 1 1] in an auto-associative net.Find
weight matrix.Test the net with [1 1 1 1] as input.
% Auto-association problem
clc;
clear;
x=[-1 -1 -1 -1;-1 -1 1 1];
t=[1 1 1 1];
w=zeros(4,4);
for i=1:2
w=w+x(i,1:4)'*x(i,1:4);
end
yin=t*w;
for i=1:4
if yin(i)>0
y(i)=1;
else
y(i)=-1;
end
end
disp('The calculated Weight Matrix');
disp(w);
if x(1,1:4)==y(1:4)| x(2,1:4)==y(1:4)
disp('The Vector is a Known vector');
else
36. disp('The Vector is a UnKnown vector');
end
Output :-
The calculated Weight Matrix
2 2 0 0
2 2 0 0
37. 0 0 2 2
0 0 2 2
The Vector is a UnKnown vector
Program No. 12
Write a MATLAB program to store the vector (1 1 1 -1).Find the weight matrix with no
self-connection.Test this using a discrete Hopfield net with mistakes in first and second
component of stored vector i.e (0 0 1 0).Also the given pattern in binary form is[1 1 1 0].
% Discrete Hopfield Net
38. clc;
clear;
x=[1 1 1 0];
tx=[0 0 1 0];
w=(2*x'-1)*(2*x-1);
for i=1:4
w(i,i)=0;
end
con=1;
y=[0 0 1 0];
while con
up=[4 2 1 3]
for i=1:4
yin(up(i))=tx(up(i))+y*w(1:4,up(i));
if yin(up(i))>0
y(up(i))=1;
end
end
if y==x
disp('Convergence has been obtained');
disp('The Converged Output');
disp(y);
con=0;
end
39. end
Output:-
up = 4 2 1 3
Convergence has been obtained
The Converged Output
1 1 1