Learning Algorithms for Neural-Net Decision Support

Artificial Neural Networks present a new paradigm for decision support that is adaptive and capable of integrating knowledge acquisition, problem solving, and learning. In this paper, we show that the performance of the classical back-propagation algorithm for training artificial neural networks can be improved by applying modified approaches which have better convergence properties, and are faster than the commonly used steepest gradient search method. Simulation results on test problems show that the proposed algorithms perform better than the steepest gradient search algorithm in terms of faster convergence with learning accuracy at least as good as in classical back propagation. Inductive learning algorithms (ID3 and NEWQ) and Probit are also used to compare the relative performances of connectionist, inductive learning and statistical procedures.

INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.