Cheat-sheetSDNEURAL Feed-forward or RBF neural network P=SDNEURAL(DATA,options) [P,RES]=SDNEURAL(DATA,options) INPUT DATA training dataset OPTIONS 'MLP' default Multi-Layer Perceptron 'units',N number of units in hidden layer (def: 10) 'iters',N number of iterations/epochs (def: 1000) 'rate',R training rate (def: 0.3) 'momentum',M momentum term (def: 0.8) 'test',TS external test set used for validation (def: [] = split DATA) 'tsfrac',N fraction of DATA used for validation (def: 0.2) 'testeach',N test each N iterations (def: 100) 'targets',MAT target matrix with the same number of samples as DATA 'noscale' do not perform default data scaling 'init',P initialize from neural net P and continue training 'RBF' Radial basis function network 'units',N Number of units per class (def: 5) 'partial' Output pipeline returns outputs per hidden unit, not class output 'gpu' train on GPU (requires parallel computing toolbox) OUTPUT P neural network pipeline RES structure describing training process ETR,ETS mean square error on training set and on test set DESCRIPTION SDNEURAL implements training of feed-forward (MLP) neural network with one hidden layer or Radial Basis Function (RBF) network. For the default feed-forward network, mean square error (MSE) is minimized with respect to the validation set (by default 20% of DATA). By default, input data set is scaled (can be disabled by 'noscale' option). It is possible to continue training starting from existing neural net pipeline P with 'init' option. Use 'noscale' and, if needed, perform scaling manually using SDSCALE. SDNEURAL may approximate the user-supplied target matrix using the 'targets' option. RBF network is trained using 'rbf' option. The number of units may be directly provided after 'rbf' option or specified with 'units' option. Default value is 5 units per class. A vector with num.of units per class may be specified. This is useful as simple unimodal classes need less units than multi-modal complex classes. The RBF network is trained with a direct formulation that does not perform iterative training. Also, there is no internal splitting of data as for MLP network. The 'partial' option returns the per-unit soft outputs instead of decisions. EXAMPLES READ MORE http://perclass.com/doc/guide/classifiers.html#sdneural
sdneural is referenced in examples: