A Characteristic-Point-Based Fuzzy Inference System Aimed to Minimize the Number of Fuzzy Rules

Abstract

This paper presents a characteristic-point-based fuzzy inference system (CPFIS) for fuzzy modeling from training data. The aim of the CPFIS is not only satisfactory precision performance, but also to employ as few purely linguistic fuzzy rules as possible by using a minimization-based systematic training method. Characteristic points (CPs) are defined as the few data points among the original training data which, when they are directly mapped to fuzzy rules and thus form the entire rule base, allow the underlying system to be effectively modeled. Three minimization-based algorithms in a sequence are proposed to train the CPFIS: a gradient-projection method, a Gauss-Jordan-elimination-based column elimination, and back-propagation. The CPs are determined by iterative computations of the first two minimization algorithms, after which the resulting fuzzy sets are further fine-tuned by the third algorithm. Experiments conducted on three benchmark problems showed that the CPFIS used one of the smallest number of fuzzy rules among the reported results for other methods. The Gaussian membership functions in both the input and output fuzzy sets and the small number of fuzzy rules make the rule interpretation of the CPFIS much easier than that of other methods, thus enhancing human-computer cooperation in knowledge discovery.