MATLAB: How to use neural networks in the diagnosis?

neural networks diagnosis application

hello I may have application to neural networks for the diagnosis? cordially.

Best Answer

 help patternnet
 doc patternnet

The targets of the d diagnoses are represented by columns of the d-dimensional unit matrix eye(d);

 Search for my classification and pattern recognition examples.
 greg patternnet
 Look at the most recent ones first.
 Hope this helps.

Thank you for formally accepting my answer

Greg

Related Solutions

MATLAB: How to train a feedfordward neural network with error weights

 Use the command window
 help mse
 doc mse

Hope this helps.

Thank you for formally accepting my answer

Greg

MATLAB: Which Network can I use for cancer diagnosis

Which Network can I use for cancer diagnosis? Asked by Shraboni Mondal on 29 Dec 2012 at 15:30

% Hello,I am new in MATLAB application and I have some doubts and queries
% also.Please suggest me.
% 
% 1> I am using NN toolbox nprtool for cancer diagnosis.I want to do some
% comparative test using other networks and algorithms.Which are the all
% networks and algorithms can I use, please suggest and also which tool can
% I use?

Tool: nprtool

Nets: Either patternnet or the obsolete newpr.

help newpr

doc newpr

help patternnet

doc patternnet

% 2>nntool also have some networks but I am getting better result using % nprtool for BPNN, why so?

The 'pr' indicates created for pattern recognition and classification

% 3> Nprtool have the option for BPNN,is it possible to use other networks % also like wise?if so,then what is the command for that?

Forget it. Only use patternnet or newpr

% 4> Does the code for one release of MATLAB varies from other?

Yes

% I am using R2008a. Is it possible se the same come for all releases?

No. Check the website documentation for BUGS and release changes

Patternnet introduced in 2010. Use newpr. Practice on the 7 sample data sets

InputMatrix: Standardize input matrix with zscore or mapstd

                   [ I N ]  =  size(input)

TargetMatrix: For c classes the target matrix columns should be columns of the c-dimensional unit matrix.

                   [ O  N ]  =  size( target)   % O = c

Classindices: classind = vec2ind(target)

Transfer Functions: { 'tansig' 'logsig' }

Training Function: 'trainscg'

Training: [ net tr ] = ntrain(net,inputs,target);

History: tr = tr

Output: y = net(input);

Assigned classes: assignedclass = vec2ind(y)

Errors: Err = (assignedclass ~= classind)

No. errors: Nerr = sum(Err)

Error Rate: PCTerr = 100*Nerr/N

To obtain separate trn/val/tst errors, use the masks and indices in tr.

Hope this helps.

Thank you for formally accepting my answer.

Greg

Best Answer

Related Solutions

MATLAB: How to train a feedfordward neural network with error weights

MATLAB: Which Network can I use for cancer diagnosis

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