Matlab Neural Network

Matlab Neural Network aims to solve several technical computing problems, consider vector formulations. Matlab programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation. Matlab is available in a number of environments such as Apple-Macintosh, VAX, PCs, sun Workstations and Microvax

 

Implementation Steps Involved In Matlab Neural Network :

 

Step 1: Different paradigm for computing

Step 2: Neural network system to be made of simple, highly interconnected processing elements

Step 3: Process information according to their dynamic state response to external inputs

Step 4: Contains learning rule used to modify the weights of connections according to the presence of input patterns

 

Architecture of  Matlab Neural Network:

Matlab Neural Network

Statistical Methods Performed in  Matlab Neural Network:

1.Principal Component Analysis

2.Linear Regression

3.K-Nearest Neighbor Classification

4.Logistic Regression

5.Discriminant Analysis

 

How to Process Matlab Neural Network:-

1.Neural network can efficiently perform the process of validation

2.Validation is a process of using part of a dataset to estimate model parameters

3.To assess the predictive ability of the model

 

Validation Methods:

1.Holdback

2.Excluded Rows

3.K-Fold

Different Classes of  Matlab Neural Networks:

1.Single Layer Feed-forward Networks

2.Multilayer Feed-forward Networks

3.Recurrent Networks

 

Learning Models using Matlab Neural Network: Method of modifying the weights of connections between the nodes of a specified network

 

Types of Learning Models:

1.Supervised Learning

2.Unsupervised Learning

3.Reinforcement Learning

Matlab Neural Network Example Code :-

Syntax:

c = cvpartition(n,’KFold’,k)
c = cvpartition(group,’KFold’,k)
c = cvpartition(n,’HoldOut’,p)
c = cvpartition(group,’HoldOut’,p)
c = cvpartition(n,’LeaveOut’)
c = cvpartition(n,’resubstitution’)

10-fold validation:

load fisheriris;

y = species;

c = cvpartition(y,’k’,10);

fun = @(xT,yT,xt,yt)(sum(~strcmp(yt,classify(xt,xT,yT))));

rate = sum(crossval(fun,meas,y,’partition’,c))…

/sum(c.TestSize)

 

 

Example Code for Feed-Forward Neural Network:

[x,t] = simplefit_dataset;net = feedforwardnet(10);net = train(net,x,t);view(net)y = net(x);perf = perform(net,y,t)

 

Example Code for Recurrent Network:

load phonemep = con2seq(y);t = con2seq(t);lrn_net = layrecnet(1,8);lrn_net.trainFcn = ‘trainbr’;lrn_net.trainParam.show = 5;lrn_net.trainParam.epochs = 50;lrn_net = train(lrn_net,p,t);