- Volume 11 Issue 3
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Characteristics of Fuzzy Inference Systems by Means of Partition of Input Spaces in Nonlinear Process
비선형 공정에서의 입력 공간 분할에 의한 퍼지 추론 시스템의 특성 분석
- Received : 2010.11.16
- Accepted : 2011.01.13
- Published : 2011.03.28
In this paper, we analyze the input-output characteristics of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods to identify the fuzzy model for nonlinear process. And 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 rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters are used for identification of fuzzy model and membership function is used as a series of triangular membership function. In the consequence part of the rules fuzzy reasoning is conducted by two types of inferences. The identification of the consequence parameters, namely polynomial coefficients, of the rules are carried out by the standard least square method. And lastly, we use gas furnace process which is widely used in nonlinear process and we evaluate the performance for this nonlinear process.
Fuzzy Inference Systems;Partition of Input Space;Min-Max Method;C-Means Clustering;Characteristics of Nonlinear Process
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