"phuong" wrote in message <firstname.lastname@example.org>... > Hi everybody, > I having a trouble with 1-step ahead of neural. > When I train network with fix parameter, I received another weight (IW,LW,b). > I know the reason is random intial weights. But why can we believe the predict result in 1-step if it alway changes for every train. May be the network not convergence. Because when it convergence, we just have only solution( or approximate solution). So is the network convergence? > All of things make the test result for 100 new predicted by neural network have many results, and some times different between so large. > Please help me fix these problems. > Thank you very much. > Phuong
The only problem is your assumption that there is only one solution. For any I-H-O network configuration with tansig hidden nodes there are (2^H)*H!-1 other nets that are equivalent. For the default value of H=10, there are (2^10)*factorial(10) = 3,715,891,200 equivalent nets. 1. There are H! equivalent nets that only differ by the way they are ordered. 2. Since tansig is an odd function, for each of those orderings there are two equivalent nets that only differ by the polarity of the weights connected to one of the H hidden nodes.
To make things worse, there can be local minima that are not global minima. The corresponding solutions range from excellent to very poor. Finally, there are other reasons (e.g., maximum mu in trainlm) that minimization searches fail.
That is why I now use Ntrials = max(10,30/Ntst) random weight initializations for each candidate value of H.