Song Ying, Chen Zengqiang, Yuan Zhuzhi
IEEE Trans Neural Netw. 2007 Mar;18(2):595-600. doi: 10.1109/TNN.2006.890809.
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.
在这封信中，提出了一种基于新型帐篷映射混沌粒子群优化算法（TCPSO）的新型非线性神经网络（NN）预测控制策略。结合帐篷映射混沌的TCPSO能够避免陷入局部极小值并提高标准粒子群优化算法（PSO）的搜索性能，被用于进行非线性优化以增强收敛性和准确性。使用两个基准函数的数值模拟来测试TCPSO的性能。此外，给出了在非线性装置上的仿真以说明所提出控制方案的有效性。
