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서선학,이철희 江原大學校 産業技術硏究所 1995 産業技術硏究 Vol.15 No.-
In this paper, we analyze the effects of scaling factors on the performance of a fuzzy controller. The quantitative relation between input and output variables of a fuzzy controller is obtained by using a quasi-linear fuzzy model. And an approximate transfer function of a fuzzy controller is derived from the comparison of fuzzy controller with the conventional PID controller. Then we analyze the effects of scaling factor using this approximate transfer function and root locus method.
이철희,서선학 江原大學校 産業技術硏究所 1996 産業技術硏究 Vol.16 No.-
In this paper,the nonlinear I/O characteristic of suzzy logic controller is analyzed by using cell concept. Sources of the conlinearity inafuzzy logic controller include thefuzzification, the fuzzy reasoning and the defuzzification. A closed form expression for the defuzzified output is derived in case of a fuzzy logic controller with two inputs, traingular memberships, MacVicar-Whelan type linguistic rules, and direct fuzzy reasoning. As a result, it is shown that fuzzy logic linguistic rules, an direct fuzzy reasoning. As a result, it is shown that fuzzy logic controller is a nonlinear controller. Also its nonlinearity is analyzed with respect to the conventional PID control and the sliding mode control.
이철희,하영기,서선학 江原大學校 産業技術硏究所 1997 産業技術硏究 Vol.17 No.-
In this paper, a new approach to modeling of nonlinear systems using fuzzy theory is presented. To express the various and complex behavior of nonlinear system, we combine multiple model method with hierachical prioritized structure, and the mountain clustering technique is used in partitioning of system. TSK rule structure is adopted to form the fuzzy rules, and Back propagation algorithm is used for learning parameters in consequent parts of the rules. Also we soften the paradigm of Mamdani's inference mechanism by using Yager's S-OWA operators. Computer simulations are performed to verify the effectiveness of the proposed method.
이철희,서선학,Yager, R. 강원대학교 정보통신연구소 1997 정보통신논문지 Vol.1 No.-
In this paper, we suggest a new approach having the flexibility to the problem of fuzzy inference and defuzzification. For fuzzy inference, we observe the fact that it consists of three parts; the determination of firing level of each rule, the determination of degree of coupling between antecedent and consequent of a rule, and the aggregation of all rules. So we soften the paradigm of Mamdani's fuzzy inference model by using S-OWA operators. Also, for defuzzification, we introduced a combinability function to help to more intelligently guide the difuzzification process. And then, we treat it as a kind of clustering problem and apply the basic idea used in the mountain clustering method. The proposed method includes Mamdani's fuzzy inference model and center of gravity method as a special case, and it has the flexibility.