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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Bibliographic Details
Published in:Iranian journal of fuzzy systems (Online) Vol. 18; no. 3; p. 179
Main Authors: Houcine, L, Bouzbida, M, Chaari, A
Format: Journal Article
Language:English
Published: Zahedan University of Sistan and Baluchestan, Iranian Journal of Fuzzy Systems 01.05.2021
Subjects:
Algorithms
Clustering
Optimization
ISSN:1735-0654, 2676-4334
Online Access:Get full text
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Summary: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