Abstract
Three improvements to gradient-based training algorithms are proposed, accelerating convergence of the conventional methods by up to two orders of magnitude. Premature saturation of hidden nodes is circumvented; weights leading to linear output nodes are updated non-recuxsively; and training of feedback structures is facilitated by two preliminary feedforward-training phases prior to the final feedback training. Performance evaluation on two simulated processes demonstrates the effect of complex search spaces on the conventional, and the new, algorithms.
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