DocumentCode
2985965
Title
Research on Nonlinear Predictive Control Rolling Optimization Strategy Based on SAPSO
Author
Bi-ying, Zhou
Author_Institution
Sch. of Math. & Inf. Sci., Weinan Teachers Univ., Weinan, China
fYear
2011
fDate
3-4 Dec. 2011
Firstpage
131
Lastpage
134
Abstract
The problem in neural network model based nonlinear systems predictive control the predictive control law is difficult to get, this paper proposed using simulated annealing particle swarm optimization algorithm (SAPSO) to optimize the solution. Compared particle swarm optimization (PSO) algorithm with SAPSO algorithm in the performance of simulation, using SAPSO algorithm optimized neural network predictive control, simulation results show that the method can effectively reduce the number of iterations and improve convergence accuracy.
Keywords
iterative methods; neurocontrollers; nonlinear control systems; particle swarm optimisation; predictive control; simulated annealing; SAPSO algorithm; iteration method; neural network model; nonlinear predictive control rolling optimization strategy; optimized neural network predictive control; predictive control law; simulated annealing particle swarm optimization algorithm; Algorithm design and analysis; Particle swarm optimization; Prediction algorithms; Predictive control; Predictive models; Simulated annealing; BP neural network; Nonlinear; Optimization; SAPSO;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Security (CIS), 2011 Seventh International Conference on
Conference_Location
Hainan
Print_ISBN
978-1-4577-2008-6
Type
conf
DOI
10.1109/CIS.2011.37
Filename
6128090
Link To Document