Tuning of Systematic Fuzzy Model by Using Evolutionary Algorithm for Control of General Systems
International Journal of Management and Fuzzy Systems
Volume 2, Issue 4, August 2016, Pages: 31-37
Received: Sep. 10, 2016; Accepted: Oct. 29, 2016; Published: Dec. 27, 2016
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Alireza Rezaee, Department of System and Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
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This paper proposes a systematic fuzzy model (SFM) to control of general system. SFM model is parted to multiple parts such as: a single parameter in formulation of reasoning; a linear relationship between input and output as a result; the use of evolutionary programming for the selection of the appropriate system parameters and a fuzzy clustering algorithm. Unlike traditional methods of inference mechanism to select a priori reasoning mechanism; SFM model can adjust its parameters using evolutionary programming. To vary the degrees of linear functions of the fuzzy rules, a set of equations describes the system’s input and output locally. Thus, this model can take advantage of the properties of linear systems. Fuzzy rules, the fuzzy c- means clustering algorithm and proper selection of the cluster centers by using evolutionary algorithm have been investigated. Finally, this system has been tested and validated on both controlled robot arm joint.
Robot, Arm, Fuzzy Clustering, Evolutionary Programming
To cite this article
Alireza Rezaee, Tuning of Systematic Fuzzy Model by Using Evolutionary Algorithm for Control of General Systems, International Journal of Management and Fuzzy Systems. Vol. 2, No. 4, 2016, pp. 31-37. doi: 10.11648/j.ijmfs.20160204.11
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