Improved fuzzy clustering algorithm using adaptive particle swarm optimization for nonlinear system modeling and identification

In this paper, an improved Type2-PCM clustering algorithm based on improved adaptive particle swarm optimization called Type2-PCM-IAPSO is proposed. Firstly, a new clustering algorithm called Type2-PCM is proposed. The Type2-PCM algorithm can solve the problems encountered by fuzzy c-means algorithm...

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Vydáno v:Iranian journal of fuzzy systems (Online) Ročník 18; číslo 3; s. 179
Hlavní autoři: Houcine, L, Bouzbida, M, Chaari, A
Médium: Journal Article
Jazyk:angličtina
Vydáno: Zahedan University of Sistan and Baluchestan, Iranian Journal of Fuzzy Systems 01.05.2021
Témata:
Algorithms
Clustering
Optimization
ISSN:1735-0654, 2676-4334
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Shrnutí:In this paper, an improved Type2-PCM clustering algorithm based on improved adaptive particle swarm optimization called Type2-PCM-IAPSO is proposed. Firstly, a new clustering algorithm called Type2-PCM is proposed. The Type2-PCM algorithm can solve the problems encountered by fuzzy c-means algorithm (FCM), Gustafson-Kessel algorithm (G-K), possibilistic c-means algorithm (PCM) and NPCM (sensitivity to noise or aberrant points and local minimal sensitivity). . . etc. Secondly, we combined our Type2-PCM algorithm with the improved adaptive particle swarm optimization algorithm (IAPSO) to ensure proper convergence to a local minimum of the objective function. The effectiveness of the two proposed algorithms Type2-PCM and Type2-PCM-IAPSO was tested on a system described by a different equation, Box-Jenkins gas furnace, dryer system and the convection system. The validation tests used showed good performance of these algorithms. However, their average square error test (MSE) shows a better behaviour of the Type2-PCM-IAPSO algorithm compared to the FCM, G-K, PCM, FCM-PSO, Type2-PCM-PSO, RKPFCM and RKPFCM-PSO algorithms.
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ISSN:1735-0654
2676-4334
DOI:10.22111/ijfs.2021.6089