Abstract— This paper shows a new fuzzy system was improved using genetic algorithm to handle fuzzy inference system as a function approximator and time series predictor. The system was developed generality that trained with genetic algorithms (GAs) corresponding to special problem and would be evaluated with different number of rules and membership functions. Then, compare the efficacy of variation of these two parameters in behavior of the system and show the method that achieves an efficient structure in both of them. Also, the proposed GA-Fuzzy inference system successfully predicts a benchmark problem and approximates an introduced function and results have been shown.
Index Terms— Genetic algorithm, fuzzy inference system, fuzzy membership functions, fuzzy rules.
V. Keikha and H. Rezaei are with the Computer Science Department, Sistan and Baluchestan University, Zahedan, Iran (e-mail: va.keikha@ yahoo.com, hrezaei@cs.usb.ac.ir).
H. Khoobipour is with the Computer Engineering Department, Azad Islamic University, Branch of Dehdasht, Dehdasht, Iran (e-mail: hayat_1378@ yahoo.com).
M. Aliyari Shoorehdeli is with the Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran (e-mail: m_aliyari@eetd.kntu.ac.ir).
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Cite: Vahideh Keikha, Member, IACSIT, Hayat Khoobipour, Mahdi Aliyari Shoorehdeli, and Hassan Rezaei, " The Scrutiny of Variation in the Number of Fuzzy Rules and Membership Functions in a New Genetic-Fuzzy System in Approximation and Prediction Problems," International Journal of Information and Electronics Engineering vol. 3, no. 5, pp. 470-475, 2013.
