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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Vydané v:International journal of fuzzy systems Ročník 22; číslo 6; s. 2011 - 2024
Hlavní autori: Houcine, Lassad, Bouzbida, Mohamed, Chaari, Abdelkader
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2020
Springer Nature B.V
Predmet:
Adaptive algorithms
Artificial Intelligence
Clustering
Computational Intelligence
Differential equations
Engineering
Fuzzy sets
Heuristic
Hydraulic equipment
Identification
Management Science
Memberships
Methods
Noise sensitivity
Nonlinear systems
Operations Research
Optimization algorithms
Particle swarm optimization
Type2-NPCM clustering algorithm
Takagi–Sugeno fuzzy modeling
Improved adaptive particle swarm optimization (IAPSO)
Fuzzy clustering
Type2-NPCM-IAPSO algorithm
NPCM algorithm
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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1562-2479
2199-3211
DOI:10.1007/s40815-020-00881-2