Bayesian regularization of neural network (Effective # param)

I wrote a code to perform Bayesian regularization of neural network using the neural network material by Martin T Hagan.

My code gives similar results with that of matlab for performance, sum squared parameter, but fails to give the same value for Effective number of parameter.

The formular i used for effective number of parameter is n - 2(alpha * trace_of_inverse_of_hessian_matrix) as described in the book.

My question is: what equation or formular does matlab use in estimating the effective number of parameter?

[uhunoma]

%%The matlab test to verify my code is given below:

x = -1:0.05:1;

t = sin(2*pi*x);% + 0.1*randn(size(x));

net = feedforwardnet(1,'trainbr');

net.trainParam.epochs = 1;

net.trainParam.mu = 0.01;

net = configure(net, x, t );

wb = getwb(net);

Nw=net.numWeightElements;

net = setwb(net,ones(Nw,1));

net.divideParam.trainRatio = 1;

net.divideParam.valRatio = 0;

net.divideParam.testRatio = 0;

net.iw{1,1}

net.b{1}

net.layers{1}.transferFcn = 'logsig';

net.layers{2}.transferFcn = 'logsig';

net = train(net,x,t)