NeuralNetworkMATLABtoolbox

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NEURAL NETWORK TOOLBOXNEURAL NETWORK TOOLBOXThe Matlab neural network toolbox provides a

complete set of functions and a graphical user interfacefor the design, implementation, visualization, andsimulation of neural networks.

It supports the most commonly used supervised andunsupervised network architectures and a comprehensive

set of training and learning functions.


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KEY FEATURESKEY FEATURES

G raphical user interface ( GU I) for creating, training, andsimulating your neural networks.

S upport for the most commonly used supervised andunsupervised network architectures.

A comprehensive set of training and learning functions.A suite of S imulink blocks, as well as documentation anddemonstrations of control system applications.

A utomatic generation of S imulink models from neuralnetwork objects.

R outines for improving generalization.


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G ENERAL CREATION OF NETWORKG ENERAL CREATION OF NETWORK

net = network

net=network(numInputs,numLayers,biasConnect,inputConnect,layerConnect,outputConnect,targetConnect)

Description

NETWORK

creates new custom networks. It is used tocreate networks that are then customized by functionssuch as NEWP, NEWLIN, NEWFF, etc.


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NETWORK takes these optional arguments (shown with

d efault values):

numInputs - N umber of inputs, 0.

numLayers - N umber of layers, 0.

biasConnect - numLayers -by -1 B oolean vector, zeros .

inputConnect - numLayers -by -numInputs Boolean matrix, zeros .

layerConnect - numLayers -by -numLayers Boolean matrix, zeros .

outputConnect - 1- by -numLayers Boolean vector, zeros .

targetConnect - 1- by -numLayers Boolean vector, zeros, an dreturns,

NET - N ew network with the given property values .


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TRAIN AND ADAPTTRAIN AND ADAPT

1 . Incremental training : updating the weights after the presentationof each single training sample.

2. Batch training : updating the weights after each presenting thecomplete data set.

When using ad apt , both incremental and batch training can be used . When using train on the other hand, only batchtraining will be used, regardless of the format of the data. The

big plus of train is that it gives you a lot more choice in training

functions (gradient descent, gradient descent w/ momentum,Levenberg-Marquardt, etc.) which are implemented veryefficiently .


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The difference between train and ad apt : the differencebetween passes and epochs . When using ad apt , the property thatdetermines how many times the complete training data set is usedfor training the network is called net .ad aptParam .passes . Fair enough. But, when using train , the exact same property is nowcalled net .trainParam .epochs .

>> net.trainFcn = 'traingdm';

>> net.trainParam.epochs = 1000;

>> net.adaptFcn = 'adaptwb';>> net.adaptParam.passes = 10;


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TRAININ G FUNCTIONSTRAININ G FUNCTIONS

There are several types of training functions:1 . S upported training functions,

2. S upported learning functions,

3. Transfer functions,

4. Transfer derivative functions,

5. Weight and bias initialize functions,

6. Weight derivative functions.


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SUPPORTED LEARNIN G FUNCTIONSSUPPORTED LEARNIN G FUNCTIONS

learncon Conscience bias learning functionlearng d G radient descent weight/bias learning functionlearng d m G radient descent with momentum weight/bias learningfunctionlearnh H ebb weight learning functionlearnh d H ebb with decay weight learning rulelearnis Instar weight learning functionlearnk K ohonen weight learning functionlearnl v1 LVQ 1 weight learning functionlearnl v2 LVQ2 weight learning functionlearnos Outstar weight learning functionlearnp Perceptron weight and bias learning functionlearnpn Normalized perceptron weight and bias learning functionlearnsom S elf-organizing map weight learning functionlearnwh Widrow- H off weight and bias learning rule


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TRANSFER FUNCTIONSTRANSFER FUNCTIONS

compet - Competitive transfer function.

har d lim - H ard limit transfer function.

har d lims - S ymmetric hard limit transfer function.

logsig - Log sigmoid transfer function.

poslin - Positive linear transfer function.

purelin - Linear transfer function.

ra d bas - R adial basis transfer function.

satlin - S aturating linear transfer function.

satlins - S ymmetric saturating linear transfer function.softmax - S oft max transfer function.

tansig - H yperbolic tangent sigmoid transfer function.

tribas - Triangular basis transfer function.


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TRANSFER DERIVATIVE FUNCTIONSTRANSFER DERIVATIVE FUNCTIONS

d har d lim - H ard limit transfer derivative function.

d har d lms - S ymmetric hard limit transfer derivative function

d logsig - Log sigmoid transfer derivative function.

d poslin - Positive linear transfer derivative function.

d purelin - H ard limit transfer derivative function.

d ra d bas - R adial basis transfer derivative function.

d satlin - S aturating linear transfer derivative function.

d satlins - S ymmetric saturating linear transfer derivative function.

d tansig - H yperbolic tangent sigmoid transfer derivative function.

d tribas - Triangular basis transfer derivative function.


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W EIGHT AND BIAS INITIALIZATION W EIGHT AND BIAS INITIALIZATION

FUNCTIONSFUNCTIONS

initcon - Conscience bias initialization function.

init zero - Zero weight/bias initialization function.

mi d point - Midpoint weight initialization function.

ran d nc - Normalized column weight initialization function.

ran d nr - Normalized row weight initialization function.

ran d s - S ymmetric random weight/bias initialization function.

W EIGHT DERIVATIVE FUNCTIONS W EIGHT DERIVATIVE FUNCTIONS

dd otpro d - Dot product weight derivative function.


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NEURAL NET W ORK TOOLBOX GUINEURAL NET W ORK TOOLBOX GUI

1 . The graphical user interface ( GU I) is designed to be simple and user friendly. This tool lets you import potentially large and complex datasets.

2. The GU I also enables you to create, initialize, train, simulate, and

manage the networks. It has theGU

I Network/Data Manager window.3. The window has its own work area, separate from the more familiar

command line workspace. Thus, when using the GU I, one might"export" the GU I results to the (command line) workspace. S imilarly to"import" results from the command line workspace to the GU I.

4. Once the Network/Data Manager is up and running, create a network,view it, train it, simulate it and export the final results to the workspace.S imilarly, import data from the workspace for use in the GU I.


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A graphical user interface can thus be used to1 . Create network,

2. Create data,3. Train the networks,

4. Export the networks,

5. Export the data to the command line workspace.


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CONCLUSIONCONCLUSION

The presentation has given an overview of Neural Network tool box in M A TL A B.

NeuralNetworkMATLABtoolbox

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