Title :
New Chaotic PSO-Based Neural Network Predictive Control for Nonlinear Process
Author :
Song, Ying ; Chen, Zengqiang ; Yuan, Zhuzhi
Author_Institution :
Dept. of Autom., Nankai Univ., Tianjin
fDate :
3/1/2007 12:00:00 AM
Abstract :
In this letter, a novel nonlinear neural network (NN) predictive control strategy based on the new tent-map chaotic particle swarm optimization (TCPSO) is presented. The TCPSO incorporating tent-map chaos, which can avoid trapping to local minima and improve the searching performance of standard particle swarm optimization (PSO), is applied to perform the nonlinear optimization to enhance the convergence and accuracy. Numerical simulations of two benchmark functions are used to test the performance of TCPSO. Furthermore, simulation on a nonlinear plant is given to illustrate the effectiveness of the proposed control scheme
Keywords :
chaos; control nonlinearities; neurocontrollers; nonlinear control systems; particle swarm optimisation; predictive control; nonlinear neural network predictive control; nonlinear optimization; particle swarm optimization; tent map chaotic PSO; Chaos; Control system synthesis; Convergence; Industrial control; Jacobian matrices; Neural networks; Optimization methods; Particle swarm optimization; Predictive control; Predictive models; Neural network (NN); nonlinear plant; particle swarm optimization (PSO); predictive control; tent-map chaos; Algorithms; Artificial Intelligence; Computer Simulation; Feedback; Information Storage and Retrieval; Models, Theoretical; Neural Networks (Computer); Nonlinear Dynamics; Pattern Recognition, Automated;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2006.890809
