Nonlinear neural network predictive control for power unit using particle swarm optimization

被引：0
|
作者：
Xiao, Jian-Mei [1 ]
Wang, Xi-Huai [1 ]
机构：
[1] Shanghai Maritime Univ, Dept Elect Engn & Automat, Shanghai 200135, Peoples R China
来源：
PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2006年
关键词：
fossil fuel power unit; nonlinear model predictive control; radial basis function neural network; particle swarm optimization; fuzzy c-mean clustering;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A novel approach of nonlinear model predictive control (NMPC) is proposed using radial basis function neural network (RBFNN) and particle swarm optimization (PSO). A multi-step predictive model of the controlled process based on RBFNN is studied. The fuzzy c-mean (FCM) clustering algorithm was used to determine the position of centers of the hidden layer of RBFNN. A modified PSO with simulated annealing is used at the optimization process in NMPC. The unit control for a fossil fuel power unit (FFPU) load system is studied. The simulation results demonstrate the effectiveness of the proposed algorithm.
引用
收藏
页码：2851 / +
页数：2
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