不确定混沌系统的直接自适应神经网络控制

Direct Adaptive Dynamic Neural Control of Unknown Chaotic System

提出了一种不确定混沌系统动态神经网络直接自适应控制方法。为了确保学习过程收敛性,研究了有效的在线学习算法,证明了闭环系统的稳定性,并针对Lorenz混沌系统进行了计算机仿真研究。

This paper presents a new direct adaptive state controller based on dynamic neural networks for unknown chaotic system. An on-line learning algorithm of neural networks is proposed to guarantee the convergence of the learning process. The stability of the closed-loop systems is proved. Simulation results are given for Lorenz chaotic system.