University of Bahrain
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Takagi-Sugeno Fuzzy System Accuracy Improvement with A Two Stage Tuning

Show simple item record Elragal,Hassan M. 2018-07-23T09:45:57Z 2018-07-23T09:45:57Z 2015
dc.identifier.issn 2210-142X
dc.description.abstract In this paper a method for improving accuracy of a Takagi-Sugeno type fuzzy system used in Matlab Fuzzy Logic Toolbox as genfis3 (Sugeno) is proposed. For this fuzzy system, the input space is partitioned using Fuzzy C-means (FCM) clustering algorithm and the consequent parameters are optimized using least square. This improvement is done in a two stage tuning using particle swarm optimization (PSO).In the first stage, PSO is used to optimize input membership functions (mean and variance) and consequent parameters of Takagi-Sugeno fuzzy system. In the second stage, PSO is used to optimize a weighting factor for the rules and a scaling universe of discourse for inputs and outputs variables. To simplify the tuning process and performing it in one optimization stage, a one stage tuning is also discussed to tune same parameters optimized in the two stage tuning. Experimental results with real data applied in data classification problem shows a consistency of getting higher classification accuracy with the proposed tuning methods over the original system. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution-NonCommercial-ShareAlike 4.0 International *
dc.rights.uri *
dc.subject Fuzzy system en_US
dc.subject Particle swarm optimization en_US
dc.subject Optimization of fuzzy system parameters en_US
dc.title Takagi-Sugeno Fuzzy System Accuracy Improvement with A Two Stage Tuning en_US
dc.type Article en_US
dc.volume 04
dc.issue 04
dc.source.title International Journal of Computing and Digital Systems
dc.abbreviatedsourcetitle IJCDS

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