Speeding Up of Fuzzy Rule Generation using GPGPU

Abstract

This paper proposes a method for speeding up the process of generating fuzzy if-then rules for pattern classification problems. The proposed method makes use of GPGPUs' parallel implementation in order to reduce the computational time. CUDA, a development environment of GPGPU, includes a library to perform matrix operations in parallel. In the proposed method, the published source codes of the matrix multiplication are modified so that the membership values of given training patterns with antecedent fuzzy sets are calculated. In the computational experiments, it is shown that the computational time is reduced for those problems that require high computational efforts.