A neuro-fuzzy system for inferencing

We justify the need for a connectionist implementation of compositional rul e of inference (COI) and propose a network architecture for the same. We ca ll it COIN-the compositional rule of inferencing. Given a relational repres entation 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 al so on choice of inferencing scheme. The problem of choosing an appropriate implication function is avoided through neural learning. The system automat ically finds an "optimal" relation to represent a set of fuzzy rules. We su ggest a suitable modeling of connection weights so as to ensure learned wei ghts lie in [0, 1]. We demonstrate through numerical examples that the prop osed 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 o nly usual COI but also Some of the previous neural implementations of fuzzy logic. (C) 1999 John Wiley & Sons, Inc.