A neuro-fuzzy system for inferencing

Abstract

We justify the need for a connectionist implementation of compositional rule of inference (COI) and propose a network architecture for the same. We call it COIN-the compositional rule of inferencing. Given a relational representation of a set of rules, the proposed architecture can realize the COI. The outcome of COI depends on the choice of the implication function and also on choice of inferencing scheme. The problem of choosing an appropriate implication function is avoided through neural learning. The system automatically finds an optimal relation to represent a set of fuzzy rules. We suggest a suitable modeling of connection weights so as to ensure learned weights lie in [0,1]. We demonstrate through numerical examples that the proposed neural realization can find a much better representation of the rules than that by usual implication and hence results in much better conclusions than the usual COI. Numerical examples exhibit that COIN outperforms not only usual COI but also some of the previous neural implementations of fuzzy logic.