> > Hi! > > I'm struggling to get an network with many neurons to learn a simple xor. > > So far ~30 neurons is ok, but if I double that I get an network that is > > more like an random number generator. > > > > The network characteristic is an fully connected network with a > > nonlinear output function. > > > > Do you have any ideas how to get an stable network that can solve xor > > and also have many number > > of neurons ? > > > XOR only requires 2 neurons if there is feedback, 3 otherwise. If you > have lots of extra neurons the network is capable of much more complex > behaviour. My suggestion is to increase the complexity of your fitness > function so that the network evolves toward the behaviour you want. > > But why do you want 30 neurons when only 3 are required for the functions? > > Are you using our XOR software from the releases, or something else?
I'm after solving a more complex and diffuse problem. But before I get there I must have some tests to prove that an large ANN can solve a simple problem like xor. I have two problems with annevolve-xor: - annevolve's xor can only handle fixed neuron count of 2. - I'm missing an simple interface that just prints the xor-table using the best ANN in the population, and doing this for every nth epoch. If you agree on these problems, I could poke with the xor code. Anyway the original question is still more interesting, and so far I'm thinking of doing an test-run foreach ANN with fixed or random input during the population initialization phase, and those ANN that isn't stable get replaced. ------------------------------------------------------------------------- Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642
