Fuzzy Classifier System and Its Applications
Fuzzy expert systems have been performing well in many fields. However, due to the problem of knowledge acquisition, the time and energy spent in constructing fuzzy expert systems are still much . Many papers have attempted to achieve automatic construction of fuzzy expert systems. For example, using genetic algorithm to solve this problem has been obtaining more and more attention. In this thesis, we try to integrate fuzzy expert systems and Holland's classifier system. We propose a fuzzy classifier system model for the automatic construction of fuzzy expert systems. In this model, we simplify the mechanism of credit assignment in classifier system. Consequently, the system is simpler and more robust. We also propose the concept of replacing line. By using this mechanism, the system performance is good and stable even when the supply of resource is low. In this thesis, we solve two problems to verify the proposed model. The first problem is the iris classification problem. The second one is the prediction of dynamical systems. When solving these two problems, we observed that fuzzy classifier system is not only effective but also stable. Furthermore, with the change of system parameters, we observed some important features of fuzzy classifier system.
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