Fuzzy reasoning implemented by neural networks

Viewing the given rule-base as defining a global linguistic association constrained by fuzzy sets, approximate reasoning is implemented by a backpropagation neural network (BNN) with the aid of the fuzzy set theory. The underlying principles are examined in detail using two examples, paying particular attention to the capability of generalization of the BNN. The simulation results indicate the feasibility of the BNN-based approach. It is demonstrated that a forward-chaining fuzzy reasoning system with parallel rule-bases can be implemented within the framework of neural networks. The studies into the BNN-based fuzzy controller suggest that, besides a seeming resemblance between rules and patterns in the logic-based and BNN-based approaches, there exists a deeper similarity in the information processing aspect in them, namely, fuzziness vs. distributiveness.<>