Part I. ANN Toolbox 0.4.2.2

Table of Contents

ANN — Toolbox for neural networks

ANN_FF — Algorithms for feedforward nets.

ANN_GEN — General utility functions

ann_FF_ConjugGrad — Conjugate Gradient algorithm.

ann_FF_Hess — computes Hessian by finite differences.

ann_FF_INT — internal implementation of feedforward nets.

ann_FF_Jacobian — computes Jacobian by finite differences.

ann_FF_Jacobian_BP — computes Jacobian trough backpropagation.

ann_FF_Mom_batch — batch backpropagation with momentum.

ann_FF_Mom_batch_nb — batch backpropagation with momentum (without bias).

ann_FF_Mom_online — online backpropagation with momentum.

ann_FF_Mom_online_nb — online backpropagation with momentum.

ann_FF_SSAB_batch — batch SuperSAB algorithm.

ann_FF_SSAB_batch_nb — batch SuperSAB algorithm (without bias).

ann_FF_SSAB_online — online SuperSAB training algorithm.

ann_FF_SSAB_online_nb — online backpropagation with SuperSAB

ann_FF_Std_batch — standard batch backpropagation.

ann_FF_Std_batch_nb — standard batch backpropagation (without bias).

ann_FF_Std_online — online standard backpropagation.

ann_FF_Std_online_nb — online standard backpropagation

ann_FF_VHess — multiplication between a "vector" V and Hessian

ann_FF_grad — error gradient trough finite differences.

ann_FF_grad_BP — error gradient trough backpropagation

ann_FF_grad_BP_nb — error gradient trough backpropagation (without bias)

ann_FF_grad_nb — error gradient trough finite differences

ann_FF_init — initialize the weight hypermatrix.

ann_FF_init_nb — initialize the weight hypermatrix (without bias).

ann_FF_run — run patterns trough a feedforward net.

ann_FF_run_nb — run patterns trough a feedforward net (without bias).

ann_d_log_activ — derivative of logistic activation function

ann_d_sum_of_sqr — derivative of sum-of-squares error

ann_log_activ — logistic activation function

ann_pat_shuffle — shuffles randomly patterns for an ANN

ann_sum_of_sqr — calculates sum-of-squares error