dmsereg (Neural Network Toolbox)

Mean squared error w/reg performance derivative function

dPerf_dE = dmsereg('e',E,X,perf,PP)

dPerf_dX = dmsereg('x',E,X,perf,PP)

dmsereg is the derivative function for msereg.

dmsereg('d',E,X,PERF,PP) takes these arguments,

E - Matrix or cell array of error vector(s).

X - Vector of all weight and bias values.

perf - Network performance (ignored).

PP - mse performance parameter.

where PP defines one performance parameters,

PP.ratio - Relative importance of errors vs. weight and bias values.

and returns the derivative dPerf/dE.

dmsereg('x',E,X,perf) returns the derivative dPerf/dX.

mse has only one performance parameter.

Examples

Here we define an error E and X for a network with one 3-element output and six weight and bias values.

E = {[1; -2; 0.5]};
X = [0; 0.2; -2.2; 4.1; 0.1; -0.2];

Here the ratio performance parameter is defined so that squared errors are 5 times as important as squared weight and bias values.

pp.ratio = 5/(5+1);

Here we calculate the network's performance, and derivatives of performance.

perf = msereg(E,X,pp)
dPerf_dE = dmsereg('e',E,X,perf,pp)
dPerf_dX = dmsereg('x',E,X,perf,pp)