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论文类型：期刊论文

发表时间：2011-09-01

发表刊物：IEEE TRANSACTIONS ON NEURAL NETWORKS

收录刊物：Scopus、SCIE、EI、PubMed

卷号：22

期号：9

页面范围：1457-1468

ISSN号：1045-9227

关键字：Chaotic map; dynamic feedforward neural network; Gaussian particle swarm optimization; predictive control; robust stability; system identification

摘要：A dynamic feedforward neural network (DFNN) is proposed for predictive control, whose adaptive parameters are adjusted by using Gaussian particle swarm optimization (GPSO) in the training process. Adaptive time-delay operators are added in the DFNN to improve its generalization for poorly known nonlinear dynamic systems with long time delays. Furthermore, GPSO adopts a chaotic map with Gaussian function to balance the exploration and exploitation capabilities of particles, which improves the computational efficiency without compromising the performance of the DFNN. The stability of the particle dynamics is analyzed, based on the robust stability theory, without any restrictive assumption. A stability condition for the GPSO + DFNN model is derived, which ensures a satisfactory global search and quick convergence, without the need for gradients. The particle velocity ranges could change adaptively during the optimization process. The results of a comparative study show that the performance of the proposed algorithm can compete with selected algorithms on benchmark problems. Additional simulation results demonstrate the effectiveness and accuracy of the proposed combination algorithm in identifying and controlling nonlinear systems with long time delays.