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(2004-5811) Improved fuzzy clustering algorithm using adaptive particle swarm optimization for nonlinear system modeling and identification | ||
| Iranian Journal of Fuzzy Systems | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 03 اسفند 1399 اصل مقاله (1.29 MB) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2021.5987 | ||
| نویسندگان | ||
| L. Houcine* 1؛ M. Bouzbida2؛ A. Chaari2 | ||
| 1Department of GTER ISET Tataouine. Laboratory for Engineering of Industrial Systems and Renewable Energies (LISIER),University of Tunisia, ENSIT, Tunisia National Higher Engineering School of Tunisia (ENSIT), BP 56, 1008, Tunisia | ||
| 2Laboratory for Engineering of Industrial Systems and Renewable Energies (LISIER), University of Tunis, ENSIT, Tunisia | ||
| چکیده | ||
| 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. | ||
| کلیدواژه‌ها | ||
| Improved adaptive particle swarm optimization (IAPSO)؛ Type2-PCM algorithm؛ Type2-PCM-IAPSO algorithm؛ fuzzy identification؛ fuzzy clustering | ||
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آمار تعداد مشاهده مقاله: 7 تعداد دریافت فایل اصل مقاله: 2 |
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