A fast procedure for the training of neural networks

Abstract

A new training paradigm for artificial neural networks is described. The technique utilizes a polynomial approximation to the sigmoidal processing function and directly integrates principal components analysis (PCA) into the network training philosophy. A major benefit of the new technique is that off-line network training is ‘one-shot’, contrary to the standard iterative techniques available in the literature. Further training may be performed on-line in a recursive fashion, yielding an adaptive neural network. Additionally, the new philosophy incorporates a systematic procedure for determining the number of neurons in the hidden layer of the network. The training procedure is first described and the implications of the training philosophy discussed. Some results, including applications to industrial chemical processes, are then presented to highlight the power of the technique. The systems considered are a continuous stirred tank reactor and a polymerization reactor.