Acquisition of fuzzy knowledge from topographic mixture networks with attentional feedback

Abstract

The topographic attentive mapping network based on a biologically-motivated neural network model is an especially effective model. When the network makes an incorrect output prediction, the attentional feedback circuit modulates the learning rates and adds a node to the category layer in order to improve the network's prediction accuracy. In this paper, a pruning method for reducing the number of category and feature nodes is formulated. We discuss the formulation and show its usefulness through some examples.