Computation scheme for the general purpose VLSI fuzzy inference engine as expert system

Abstract

Fuzzy inference engines based on the existing fuzzy theory are inadequate to perform reliable decision making. Besides requiring the fuzzy sets and data to be normalized, the inference engine is also sensitive to noise in observational data. Inaccurate conclusions are produced if noise is present and also when the fuzzy sets are not normalized. In this paper, a new term 'similarity' (s) and the method to compute s to enhance the capability of fuzzy set theory for application in expert systems is introduced. Even though the complexity of the hardware engine is slightly increased, it actually reduces the overhead of computation by eliminating the need for normalization of fuzzy data. With reliable fuzzy data manipulation, it is easy to extend to a multi-dimensional membership function which has a wider scope of applications. To To implement the Very Large Scale Integration fuzzy inference engine, two general schemes of the hardware architecture that van be easily reconfigured to satisfy given performance requirements are discussed.