Fuzzy Logic and Intelligent Systems pp 311-366 | Cite as

# Automatic Optimal Design of Fuzzy Systems Based on Universal Approximation and Evolutionary Programming

## Abstract

The theory of fuzzy sets was initiated in 1965 by Zadeh [1]. Since then, it has been expanded and has found its way into a large number of applicationa, for exmaple in the areas of experts systems and control (Mamdani 1975 was the pioneer in control [2]). For several applications areas, there are many examples of solutions based on fuzzy sets that outperform traditional solutions, exmplaes in control are for control of cemnet kiln [3], car parking [4] and automatic train operation [5]. The reason fof this is its unique properties of handling nonlinearity as well as uncertainty. An advantage of fuzzy systems is that they are based on linguistic rules, and therefore it is often easy to understand the underlying fucntionality of the systems. This is of great help when these systems are designed, because expert knowledge and common sense can often contributre in a catural way. However, sometimes, sufficient knowledge is not available, which makes the design phase difficult. Also the process of designing fuzzy systems is usually heuristic, and systematic, approaches that lead to optimal systems do not exist. This chapter describes progress towards an universal systematic design strateggy for automatically producing optimal fuzzy systems. In this strategy, the fuzzy system is viewed as an universal approximator [10] and optimized by using Guided Evolutionary Simulated Annealing (GESA) [23]. This design strategy is tested on two applications: general function approximation and control.

## Keywords

Membership Function Fuzzy System Fuzzy Logic Controller Inverted Pendulum Fuzzy Partition## Preview

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