Indian Journal of Science and Technology
DOI: 10.17485/ijst/2016/v9i43/104401
Year: 2016, Volume: 9, Issue: 43, Pages: 1-10
Original Article
Arpit Jain1 *, Satya Sheel2 and Kamal Bansal3
1Electronics and Instrumentation Engineering, University of Petroleum and Energy Studies, Dehradun - 248007, Uttarakhand, India; [email protected] 2Electrical Engineering Department, GLA University, Mathura – 281406, Uttar Pradesh, India 3Electrical, Power and Energy Engineering, University of Petroleum and Energy Studies, Dehradun - 248007, Uttarakhand, India
*Author for correspondence
Arpit Jain
Electronics and Instrumentation Engineering, University of Petroleum and Energy Studies, Dehradun - 248007, Uttarakhand, India;
Email: [email protected]
This paper presents a Genetic Algorithm based optimization algorithm for fuzzy logic system. The proposed algorithm employs fuzzy entropy function as optimization bound(s) parameter. Membership function formation plays a key role in performance of a fuzzy system, as an improperly designed MF may lead to an inefficient system. Majority of literature focuses on optimization of shape of a MF and not the support. Proposed optimization method focuses on optimizing support of MF and not on its shape. For this optimization process predefined membership functions are used, the support of these membership functions these predefined sets are varied using standard deviation of the system data obtained by simulation or real-time analysis of the system. The support of these membership functions these predefined sets are varied using standard deviation of the system data obtained by simulation or real-time analysis of the system. Entropy for each displaced Fuzzy Set is maximized subjective to constraint optimization and thus optimized value of support is obtained. The proposed algorithm optimizes the support of Fuzzy Sets and hence can be combined with any other optimization tool for obtaining even better results.
Keywords: Fuzzy Entropy, Genetic Algorithms, Support Optimization
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