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It is well known that the process of tuning a fuzzy logic controller is almost always a very complex task, which is time consuming, very laborious and often requires expert knowledge of the controlled system. Mapping of fuzzy logic controller's parameters (rule base and membership functions of input(s) and output(s)) into a performance measure in a closed analytical form is near impossible to get,...
This paper proposes a new method to design a fuzzy logic controller by genetic algorithms (GAs). The basic idea of this new methodology is to use a new way to mutate the chromosome. Bio-process control problem is used to compare the proposed mutation to traditional mutation. The results show that the proposed mutation efficiently improves GA's performance.
Fuzzy measure is subjective scale for the degrees of fuzziness and suitable for analyzing human subjective evaluation processes. It is not easy to provide consistent fuzzy measure values with fuzzy measure properties since they have to be subjective determined. Thus it induces an identification problem that determines measure values with fuzzy measure properties from human-provided. The λ-fuzzy measure...
This paper introduces genetic algorithm (GA) based method to construct a satisfactory fuzzy system directly from some gathered input-output data of the discussed problem. In this method, each individual in the population is constructed to determine the number of fuzzy rules and the premise and the consequent part of the fuzzy system. With the special mutation operation and the adequate fitness function,...
Fuzzy logic based methods together with the techniques from Artificial Intelligence have gained importance. Association rules together with fuzzy logic to model the fuzzy association rules are being used for classifying data. These together with the techniques of genetic algorithms like genetic programming are producing better results. Therefore, in this article, we firstly analyze the current situation...
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