Hello Noor,
For some functions like the 'trainlm' function, they not support the 'mae' performance function.
The best way to create a custom performance function is to use MSE.M and the +MSE package of functions as a template. Here are descriptions of the required package functions:
newfcn.m - Same as mse.m
+newfcn/apply.m - The main performance calculation function perfs = apply(t,y,e,param) Calculate performance for each target individually so perfs is same size as t, y and e.
+newfcn/backprop.m - Backprop derivatives function dy = backprop(t,y,e,param) Return dperf/dy, the derivative of performance with respect to each output y.
+newfcn/forwardprop.m - Forward propagate derivatives function dperf = forwardprop(dy,t,y,e,param) Return dperf/dwb given dy/dwb.
+newfcn/type.m - Indicate that "newfcn" is a performance function. function t = type t = 'performance_fcn';
+newfcn/name.m function name = name() Return the name of the function, for instance: name = 'My New Peformance Function';
+newfcn/normalize.m function flag = normalize Return true if mean performance is desired (i.e. mean squared error, mean absolute error) and false if the absolute performance is desired (i.e. sum squared error, sum absolute error).
+newfcn/parameterInfo.m function param = parameterInfo Return the same array of parameter definitions as MSE. Customer can also add additional parameters if desired.
+newfcn/perfwb.m - Regularization performance used to minimize weights and biases function perf = perfwb(wb,param) Return the performance measure for weights and biases. This performance measure is only used when net.performParam.regularization is set to a value greater than 0. If regularization is not going to be used this function can return 0.
+newfcn/dperf_dwb.m - Regularization derivatives function dperf = dperf_dwb(wb,param) If you are not doing regularization then this function can return zeros the same size as wb. Otherwise it should return a derivative of regularization performance (calculated by perfwb) with respect to each weight and bias value in wb.
*There is one more caveat when following this approach in R2012b – there is an issue that is planned to be resolved in a future release, but currently defining custom functions with this approach works only with the non-MEX version of the Neural Network code, so it is necessary to call TRAIN with a special syntax – i.e., using the nn7 option. This workaround is the following syntax for training and simulating the network:
net = train(net,x,t,nn7);
y = net(x,nn7);
