Hierarchical Fuzzy Systems

Abstract

Fuzzy inference systems [200], [201], [225] have been successfully applied to a number of scientific and engineering problems during recent years. The advantage of solving complex nonlinear problems by utilizing fuzzy logic methodologies is that the experience or expert’s knowledge described as the fuzzy rule base can be directly embedded into the system for dealing with the problems. Many efforts have been made to enhance systematic design of fuzzy logic systems [203], [204], [205], [206], [207], [239], [244]. Some research focus on automatically finding the appropriate structure and parameters of fuzzy logic systems by using genetic algorithms [204], [207], [239], evolutionary programming [206], tabu search [208], and so on. There are many research works focusing on partitioning of the input space, to determine the fuzzy rules and parameters evolved in the fuzzy rules for a single fuzzy system [232], [229]. As it is well known, the curse-of-dimensionality is an unsolved problem in the fields of fuzzy and/or neuro-fuzzy systems [243].