Hybrid Systems: Synergies of Fuzzy, Neural and Evolutionary Computing Evolutionary Fuzzy Systems

Although fuzzy logic systems have been successfully applied in many complex industrial processes, they experience a deficiency in knowledge acquisition and rely to a great extent on empirical and heuristic knowledge, which, in many cases, cannot be objectively elicited. One of the most important considerations in designing fuzzy systems is construction of the membership functions for each fuzzy set as well as the rule-base. In most existing applications, the fuzzy rules are generated by an expert in the area, especially for the control problems with only a few inputs. The correct choice of membership functions is by no means trivial but plays a crucial role in the success of an application. Previously, generation of membership functions had been a task mainly done either interactively, by trial and error, or by human experts. With an increasing number of inputs and linguistic variables, the possible number of rules for the system increases exponentially, which makes it difficult for experts to define a complete set of rules and associated membership functions for a good system performance. An automated way to design fuzzy systems might be preferable.