Improved Type2-NPCM Fuzzy Clustering Algorithm Based on Adaptive Particle Swarm Optimization for Takagi–Sugeno Fuzzy Modeling Identification

In this paper, an improved Type2-NPCM clustering algorithm based on improved adaptive particle swarm optimization called Type2-NPCM-IAPSO is proposed. First, a new clustering algorithm called Type2-NPCM is proposed. The Type2-NPCM algorithm can solve the problems encountered by the algorithms FCM, G...

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Vydáno v:International journal of fuzzy systems Ročník 22; číslo 6; s. 2011 - 2024
Hlavní autoři: Houcine, Lassad, Bouzbida, Mohamed, Chaari, Abdelkader
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2020
Springer Nature B.V
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ISSN:1562-2479, 2199-3211
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Shrnutí:In this paper, an improved Type2-NPCM clustering algorithm based on improved adaptive particle swarm optimization called Type2-NPCM-IAPSO is proposed. First, a new clustering algorithm called Type2-NPCM is proposed. The Type2-NPCM algorithm can solve the problems encountered by the algorithms FCM, G-K, PCM and NPCM (sensitivity to noise or aberrant points and local minimal sensitivity), etc. Second, we combined our Type2-NPCM algorithm with the improved adaptive particle swarm optimization IAPSO algorithm to ensure proper convergence to a local minimum of the objective function. The effectiveness of the proposed Type2-NPCM-IAPSO algorithm was tested on the electro-hydraulic system, convection system and other nonlinear systems described by differential equation.
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ISSN:1562-2479
2199-3211
DOI:10.1007/s40815-020-00881-2