bias problem with trainb

I need to train a supervised neural network to linearly separate a set of inputs with logical output 0,1 in batch training. My inputs is inputstemp2 (100,2000) of double precision & target is dividetargets(2,2000) of double 0 & 1. My code is :

         mynet=perceptron;
         mynet.trainFcn=  'trainb';
         mynet.inputWeights.learnFcn='learnp';   %default
         mynet.biases.learnFcn= 'learnp';        %default
         mynet.biasConnect =[1];
         mynet.outputs{1}.processFcns = {};
         mynet.inputs{1}.processFcns = {'mapstd','processpca'};
         mynet.divideFcn = 'divideblock';
         mynet.divideParam.trainRatio = 80/100;
         mynet.divideParam.valRatio = 10/100;
         mynet.divideParam.testRatio =10/100;
         mynet.trainParam.showWindow = false;
         mynet.trainParam.showCommandLine = false;
         mynet.trainParam.epochs=500;
         mynet.efficiency.memoryReduction=1;
        [mynet,tr]=train(mynet,inputstemp2,dividetargets);

No problem if mynet.biasConnect =[0] but if I set mynet.biasConnect =[1] Matlab return:

Error using  * 
Inner matrix dimensions must agree.

Error in learnp>apply (line 93)
  dw = e*p';

Error in trainb>train_network (line 231)
            [db,BLS{i}] = learnFcn.apply(net.b{i}, ...

Error in trainb (line 55)
        [out1,out2] = train_network(varargin{2:end});

ecc.... The same if I use linearlayer and harlim. Somebody can explain me why bias is not accepted?

Answer 1

[Greg Heath]

Just use the current (i.e., non-obsolete) classifier

 net = patternnet([]); % [] => No hidden layer for linear model

For documentation

 help patternnet
 doc  patternnet

Hope this helps.

Thank you for formally accepting my answer

Greg

[Emiliano Rosso]

Solved & best convergence.

Thanks.