FANRE: A fast adaptive neural regression estimator

Abstract

In this paper, a fast adaptive neural regression estimator named FANRE is proposed. FANRE exploits the advantages of both Adaptive Resonance Theory and Field Theory while contraposing the characteristic of regression problems. It achieves not only impressive approximating results but also fast learning speed. Besides, FANRE has incremental learning ability. When new instances are fed, it does not need retrain the whole training set. In stead, it could learn the knowledge encoded in those instances through slightly adjusting the network topology when necessary. This characteristic enable FANRE work for real-time online learning tasks. Experiments including approximating line, sine and 2-d Mexican Hat show that FANRE is superior to BP kind algorithms that are most often used in regression estimation on both approximating effect and training time cost.