Mixing Fuzzy, Neural and Genetic Algorithms in an Integrated Design Environment for Intelligent Controllers

1 In the last years fuzzy logic has gained the interest of a growing part of the scienti c community, due to some interesting properties which characterize it as against other kinds of Arti cial Intelligence paradigms. In our view the most interesting properties of fuzzy logic are: a) its similarity with the human reasoning; b) its implicit robustness, coming from the fact that it directly expresses input/output relationships, with no need for or physical derivation of the rules; c) its relatively small computational complexity, which makes it particularly suitable for real-time applications. The strength of fuzzy logic comes from the fact that it can mix linguistic rules with eld-acquired data and, under given conditions, there are no limits to the kind of control surfaces which can be implemented [1]. This permits a convenient application of fuzzy logic in those control systems where the plant to be controlled shows lack of linearity, and in particular whenever there is a consistent amount of human expertise (often coming from the experience) about the way the system has to be controlled. This expertise can be converted into rules which de ne the optimal control surface for the plant. Once a rst model of a Fuzzy Controller (FC) has been de ned, it is necessary to set its parameters in order to obtain the desired performance. This can be done in several ways, and optimization techniques like Simulated Annealing, Weight Perturbation, or Genetic Algorithms [2, 3] can be used.