A neuro-fuzzy framework for inferencing.

ABSTRACT

Earlier we proposed a connectionist implementation of compositional rule of inference (COI) for rules with antecedents having a single clause. We first review this net, then generalize it so that it can deal with rules with antecedent having multiple clauses. We call it COIN, the compositional rule of inferencing network. 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 both the implication function and the inferencing scheme. The problem of choosing an appropriate implication function is avoided through neural learning. COIN can automatically find a 'good' relation to represent a set of fuzzy rules. We model the connection weights so as to ensure learned weights lie in [0,1]. We demonstrate through extensive 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.