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Introduction To Neural Networks Using Matlab 6.0 .pdf


Introduction to Neural Networks Using MATLAB 6.0




Neural networks are computational models that mimic the structure and function of biological neurons. They can learn from data and perform tasks such as classification, regression, clustering, and pattern recognition. Neural networks have many applications in various fields, such as bioinformatics, robotics, communication, image processing, and healthcare.


MATLAB is a powerful software tool that allows easy and fast manipulation, visualization, and analysis of data. It also provides a lot of built-in functions and toolboxes for implementing different types of neural networks. In this article, we will introduce some basic concepts and examples of neural networks using MATLAB 6.0.


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Fundamental Models of Artificial Neural Networks




An artificial neural network consists of a set of interconnected units called neurons or nodes. Each neuron receives inputs from other neurons or external sources, performs a weighted sum of the inputs, and applies a nonlinear activation function to produce an output. The weights of the connections determine the strength and direction of the influence between neurons. The activation function determines the output range and the nonlinear behavior of the neuron.


There are different types of neural network architectures, depending on how the neurons are arranged and connected. Some common architectures are:



  • Feedforward neural networks: The neurons are organized in layers, and the connections are directed from one layer to the next. There are no feedback loops or cycles in the network. The input layer receives the external inputs, the output layer produces the network outputs, and the hidden layers perform intermediate computations.



  • Recurrent neural networks: The neurons are connected in a way that allows feedback loops or cycles in the network. This means that some neurons can receive their own outputs or outputs from other neurons as inputs. Recurrent neural networks can store information over time and capture temporal dependencies in the data.



  • Self-organizing neural networks: The neurons are arranged in a grid or a map, and they adjust their weights based on the similarity or distance between their inputs and outputs. Self-organizing neural networks can learn to cluster or group similar data points without supervision.




Perceptron Networks




A perceptron is a simple type of feedforward neural network that consists of a single layer of neurons with binary outputs (0 or 1). A perceptron can learn to perform linearly separable classification tasks, such as AND, OR, and XOR logic gates. The learning algorithm for a perceptron is based on updating the weights according to the error between the desired output and the actual output for each input pattern.


In MATLAB 6.0, we can use the function newp to create a perceptron network with a specified number of inputs and outputs. For example, we can create a perceptron network with two inputs and one output as follows:


```matlab net = newp([-1 1; -1 1],1); % create a perceptron with two inputs in [-1,1] range and one output ``` We can then train the network using the function train with some input-output pairs. For example, we can train the network to perform an XOR logic gate as follows:


```matlab P = [-1 -1 1 1; -1 1 -1 1]; % input patterns T = [1 -1 -1 1]; % desired outputs net = train(net,P,T); % train the network ``` We can then test the network using the function sim with some new input patterns. For example, we can test the network with the same input patterns as follows:


```matlab P = [-1 -1 1 1; -1 1 -1 1]; % input patterns Y = sim(net,P); % simulate the network ``` The output Y should be close to T if the network has learned correctly.


Adaptive Resonance Theory




Adaptive resonance theory (ART) is a type of self-organizing neural network that can learn to cluster or categorize data in an unsupervised manner. ART networks have two layers: an input layer (F1) and a competitive layer (F2). The F2 layer consists of a fixed number of clusters or categories, each represented by a prototype vector. The learning algorithm for an ART network is based on finding a match between an input pattern and a prototype vector, and updating the prototype vector if the match is good enough. The match is determined by a vigilance parameter, which controls the degree of similarity or dissimilarity between the input and the prototype.


In MATLAB 6.0, we can use the function newart to create an ART network with a specified number of inputs and clusters. For example, we can create an ART network with two inputs and four clusters as follows:


```matlab net = newart([-1 1; -1 1],4); % create an ART network with two inputs in [-1,1] range and four clusters ``` We can then train the network using the function train with some input patterns. For example, we can train the network with some random input patterns as follows:


```matlab P = 2*rand(2,100)-1; % generate 100 random input patterns in [-1,1] range net = train(net,P); % train the network ``` We can then test the network using the function sim with some new input patterns. For example, we can test the network with the same input patterns as follows:


```matlab P = 2*rand(2,100)-1; % generate 100 random input patterns in [-1,1] range Y = sim(net,P); % simulate the network ``` The output Y is a matrix that indicates which cluster each input pattern belongs to. For example, if Y(3,5) is 1, it means that the fifth input pattern belongs to the third cluster.


Conclusion




In this article, we have introduced some basic concepts and examples of neural networks using MATLAB 6.0. We have shown how to create, train, and test different types of neural networks, such as perceptron networks and ART networks. We have also used some built-in functions and toolboxes that provide a lot of features and options for implementing neural networks. For more information and details, we refer to the official documentation and some books on neural networks using MATLAB.


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