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

In this paper, an improved Type۲-PCM clustering algorithm based on improved adaptive particle swarm optimization called Type۲-PCM-IAPSO is proposed. Firstly, a new clustering algorithm called Type۲-PCM is proposed. The Type۲-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 Type۲-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 Type۲-PCM and Type۲-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 Type۲-PCM-IAPSO algorithm compared to the FCM, G-K, PCM, FCM-PSO, Type۲-PCM-PSO, RKPFCM and RKPFCM-PSO algorithms.

Keywords:

Improved adaptive particle swarm optimization (IAPSO) , Type۲-PCM algorithm , Type۲-PCM-IAPSO algorithm , fuzzy identification , Fuzzy clustering