Approximate case-based reasoning on neural networks

Abstract

In this paper, based on the deeper analysis of the features of fuzzy logic and approximate reasoning, the concept of approximate case-based reasoning (ACBR) is introduced. According to the inference mechanism of ACBR, an implementation on neural networks is proposed. Mapping the implication relation between the premise(s) and the consequence of a fuzzy rule to the weight of a corresponding neural network unit, an approximate case-based reasoning on neural networks can be realized. The self-organizing and self-learning procedure can be executed by modifying the weight. © 1994.