Design of an Effective Parallel Architecture for Fuzzy Information Processing

Abstract

In this paper, we present parallelizing techniques for the two fuzzy inference methods (Mamdani and Koczy methods), and propose an effective parallel fuzzy architecture, which is suitable for fuzzy information processing. The proposed parallel fuzzy architecture is composed of FPEs, CP, memory modules, interconnection network and MIN circuit. This achieves a relatively higher performance and is a generalized cascadable architecture independent of input variables, output variables and rule base. In this, each FPE_i processes only the operations of the i-th antecedent and the i-th consequent. Then, all of the antecedents in the condition part are executed in parallel, same as in the consequent part. So processors can be fully utilized and it is easy to configure the design with any number of antecedents, consequents and rules. This parallel fuzzy inference architecture can be used in a system requiring a rapid inference time in real-time system and/or a large expert system that has many inference variables in the condition part and the consequent part.