[1]LIU Nan,LIU Fucai,MENG Aiwen.Fuzzy identification based on improved PSO and FCM[J].CAAI Transactions on Intelligent Systems,2019,14(2):378-384.[doi:10.11992/tis.201707025]
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Fuzzy identification based on improved PSO and FCM

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 2 Page number: 378-384 Column: 学术论文—智能系统 Public date: 2019-03-05
Title:
Fuzzy identification based on improved PSO and FCM
Author(s):
LIU Nan;  LIU Fucai;  MENG Aiwen
College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
Keywords:
fuzzy identification; nonlinear system; fuzzy C-means; T-S model; intelligent algorithm; particle swarm optimization; Box-Jenkins identification; global optimization
CLC:
TP15
DOI:
10.11992/tis.201707025
Abstract:
To improve the accuracy and efficiency of T-S model, a new fuzzy identification approach based on improved particle swarm optimization (PSO) algorithm and fuzzy C-means (FCM) algorithm is proposed. Considering that it is easy for PSO to fall into the local extremum in the treatment of high-dimensional complex functions, a PSO algorithm with dynamic adjustment between local search and global search is proposed in this paper. Moreover, the FCM algorithm is one of the most commonly used methods of fuzzy identification. The algorithm is simple and efficient, but it is particularly sensitive to initialization and easily falls into local optimum. To solve this problem, the global search capability of improved PSO is used to optimize the clustering center, and this significantly improves the accuracy and efficiency of the algorithm. Finally, a nonlinear system is modeled and simulated. The simulation results show the effectiveness and superiority of this method.
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