Improved fuzzy clustering algorithm using adaptive particle swarm optimization for nonlinear system modeling and identification
نویسندگان
1 Laboratory for Engineering of Industrial Systems and Renewable Energies (LISIER), University of Tunis, ENSIT, Tunisia
2 Laboratory for Engineering of Industrial Systems and Renewable Energies (LISIER), University of Tunis, ENSIT, Tunisia
3 Department 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
doi
10.22111/ijfs.2021.6089چکیده
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.
