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This paper proposes an Enhanced Particle Swarm Optimization (EPSO) for extracting optimal rule set and tuning membership function for fuzzy logic based classifier model. The standard PSO is more sensitive to premature convergence due to lack of diversity in the swarm and can easily get trapped into local minima when it is used for data classification. To overcome this issue, BLX-α crossover and Non-uniform mutation from Genetic Algorithm (GA) are incorporated in addition to standard velocity and position updating of PSO. The performance of the proposed approach is evaluated using ten publicly available bench mark data sets. From the simulation study, it is found that the proposed approach enhances the convergence and generates a comprehensible fuzzy classifier system with high classification accuracy for all the data sets. Statistical analysis of the test result shows the suitability of the proposed method over other approaches reported in the literature.

An important issue in the design of FRBS is the formation of fuzzy if-then rules and the membership functions. This paper presents a Mixed Genetic Algorithm (MGA) approach to obtain the optimal rule set and the membership function of the fuzzy classifier. While applying genetic algorithm for fuzzy classifier design, the membership functions are represented as real numbers and the fuzzy rules are represented as binary string. Modified forms of crossover and mutation operators are proposed to deal with the mixed string. The proposed genetic operators help to improve the convergence of GA and accuracy of the classifier. The performance of the proposed approach is evaluated through development of fuzzy classifier for seven standard data sets. From the simulation study it is found that the proposed algorithm produces a fuzzy classifier with minimum number of rules and high classification accuracy. Statistical analysis of the test results shows the superiority of the proposed algorithm over the existing methods.

When the number of input dimension is large, the conventional fuzzy neural systems often cannot handle the task correctly because the degree of each rule becomes too small. In this paper, a new adaptive fuzzy inference neural network (AFINN) based on full implication triple-I fuzzy inference method has been described. It has fuzzy weights and accepts fuzzy set inputs. The advantages of AFINN are that it has initial rule creation ability and fuzzy inference ability, the degree of each rule doesn't need to be calculated. This system also automatically generates rules from numerical data. The proposed system operates with Gaussian membership functions in premise and conclusion parts. Euclidian distances are used to parameter estimation and initialization of unknown parameter values. For evaluation of the number of if-then rules, the standard RMSE performance index have been applied. The applications to prediction of chaotic time series is considered in this paper as well.