newff
newff Create a feed-forward backpropagation network. Obsoleted in R2010b NNET 7.0. Last used in R2010a NNET 6.0.4. The recommended function is feedforwardnet. Syntax net = newff(P,T,S) net = newff(P,T,S,TF,BTF,BLF,PF,IPF,OPF,DDF) Description newff(P,T,S) takes, P - RxQ1 matrix of Q1 representative R-element input vectors. T - SNxQ2 matrix of Q2 representative SN-element target vectors. Si - Sizes of N-1 hidden layers, S1 to S(N-1), default = []. (Output layer size SN is determined from T.) and returns an N layer feed-forward backprop network. newff(P,T,S,TF,BTF,BLF,PF,IPF,OPF,DDF) takes optional inputs, TFi - Transfer function of ith layer. Default is 'tansig' for hidden layers, and 'purelin' for output layer. BTF - Backprop network training function, default = 'trainlm'. BLF - Backprop weight/bias learning function, default = 'learngdm'. PF - Performance function, default = 'mse'. IPF - Row cell array of input processing functions. Default is {'fixunknowns','remconstantrows','mapminmax'}. OPF - Row cell array of output processing functions. Default is {'remconstantrows','mapminmax'}. DDF - Data division function, default = 'dividerand'; and returns an N layer feed-forward backprop network. The transfer functions TF{i} can be any differentiable transfer function such as TANSIG, LOGSIG, or PURELIN. The training function BTF can be any of the backprop training functions such as TRAINLM, TRAINBFG, TRAINRP, TRAINGD, etc. *WARNING*: TRAINLM is the default training function because it is very fast, but it requires a lot of memory to run. If you get an "out-of-memory" error when training try doing one of these: (1) Slow TRAINLM training, but reduce memory requirements, by setting NET.efficiency.memoryReduction to 2 or more. (See HELP TRAINLM.) (2) Use TRAINBFG, which is slower but more memory efficient than TRAINLM. (3) Use TRAINRP which is slower but more memory efficient than TRAINBFG. The learning function BLF can be either of the backpropagation learning functions such as LEARNGD, or LEARNGDM. The performance function can be any of the differentiable performance functions such as MSE or MSEREG. Examples [inputs,targets] = simplefitdata; net = newff(inputs,targets,20); net = train(net,inputs,targets); outputs = net(inputs); errors = outputs - targets; perf = perform(net,outputs,targets) Algorithm Feed-forward networks consist of Nl layers using the DOTPROD weight function, NETSUM net input function, and the specified transfer functions. The first layer has weights coming from the input. Each subsequent layer has a weight coming from the previous layer. All layers have biases. The last layer is the network output. Each layer's weights and biases are initialized with INITNW. Adaption is done with TRAINS which updates weights with the specified learning function. Training is done with the specified training function. Performance is measured according to the specified performance function.