A Simplified Structure Evolving Method for Fuzzy System structure learning

This paper proposes a Simplified Structure Evolving Method (SSEM) for Fuzzy Systems, which improves our previous work of Structure Evolving Learning Method for Fuzzy Systems (SELM [1]). SSEM keeps all the advantages of SELM [1] and improve SELM by starting with the simplest fuzzy rule set with only one fuzzy rule (instead of 2ⁿ fuzzy rules in SELM) as the starting point. By doing this SSEM is able to select the most efficient partitions and the most efficient attributes as well for system identification. This improvement enables fuzzy systems applicable to high dimensional problems. Benchmark examples with high dimension inputs are given to illustrate the advantages of the proposed algorithm.

I. Introduction

Since the invention of fuzzy sets and fuzzy systems by Zadeh four decades ago, fuzzy systems have been successfully applied in different areas. These successes are grounded on the advantages of fuzzy systems [2]–[12]. Firstly, fuzzy systems can be identified by combining human knowledge and information from data. Secondly, the transparency and interpretability of fuzzy rules and systems make them easy to understand and use in applications, thirdly, fuzzy systems are universal approximators which can approximate any continuous nonlinear function to any degree of accuracy and therefore are applicable to be used to solve general complex and nonlinear problems.