ann_FF_Std_online_nb — online standard backpropagation
x Matrix of input patterns, one pattern per column t Matrix of targets, one pattern per column. Each column have a correspondent column in x. N Row vector describing the number of neurons per layer. N(1) is the size of input pattern vector, N(size(N,'c')) is the size of output pattern vector (and also target). W The weight hypermatrix (initialized first trough ann_BP_init_nb). lp Learning parameters [lp(1),lp(2)]. lp(1) is the well known learning parameter of standard backpropagation algorithm, W is changed according to the formula: W(t+1) = W(t) - lp(1) * grad E where t is the (discrete) time and E is the error. Typical values: 0.1 ... 1. Some networks train faster with even greater learning parameter. lp(2) defines the threshold of error which is backpropagated: a error smaller that lp(2) (at one neuronal output) is rounded towards zero and thus not propagated. Typical values: 0 ... 0.1. E.g. assume that output of neuron n have the actual output 0.91 and the target (for that particular neuron, given the corresponding input) is 1. If lp(2) = 0.1 then the error term associated to n is rounded to 0 and thus not propagated. T The number of epochs (training cycles trough all pattern set). af Activation function and its derivative. Row vector of strings: af(1) name of activation function. af(2) name of derivative. Warning: given the activation function y=f(x), the derivative have to be expressed in terms of y, not x. This parameter is optional, default value is "['ann_log_activ', 'ann_d_log_activ']", i.e. logistic activation function and its derivative. err_deriv_y the name of error function derivative with respect to network outputs. This parameter is optional, default value is "ann_d_sum_of_sqr", i.e. the derivative of sum-of-squares. ex two-dimensional row vector of strings representing valid Scilab sequences. ex(1) is executed after the weight hypermatrix have been updated, after each pattern (not whole set), using execstr. ex(2) - same as ex(1) - but is executed after each epoch. This parameter is optional, default value is [" "," "] (do nothing).