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http://dx.doi.org/10.5392/JKCA.2011.11.4.074

Characteristics of Input-Output Spaces of Fuzzy Inference Systems by Means of Membership Functions and Performance Analyses  

Park, Keon-Jun (원광대학교 정보통신공학과)
Lee, Dong-Yoon (중부대학교 전기전자공학과)
Publication Information
The Journal of the Korea Contents Association / v.11, no.4, 2011 , pp. 74-82 More about this Journal
Abstract
To do fuzzy modelling of a nonlinear process needs to analyze the characteristics of input-output of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods. For this, fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the fuzzy rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the clusters are used for identification of fuzzy model and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. In the consequence part of the fuzzy rules fuzzy reasoning is conducted by two types of inferences such as simplified and linear inference. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. And lastly, using gas furnace process which is widely used in nonlinear process we evaluate the performance and the system characteristics.
Keywords
Membership Function; Triangular; Gaussian-Like; Trapezoid-Type; Standard Least Square Method;
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