Neural network based competitive learning for control

Abstract

The idea of competitive learning for pattern-recognition applications is introduced. A brief review of two competitive learning models, T. Kohonen's self-organizing feature maps (1982, 1989) and S. Grossberg's ART networks (1987), is presented. Neural-net-based partitioning algorithms for learning control are introduced. A simulation study, of these algorithms incorporated into the BOXES machine learning control system is reported. Simulation results are presented and performance comparisons are made, using the BOXES algorithm as the standard, with the new neural-net-based partitioning method. The original BOXES partitioning method of fixed threshold quantization of state-space variables was used in the BOXES algorithm learning trials.<>