Chaotifying Unknown Dynamical Systems via Feedback Control Based on Neural Networks
In this paper, we study the problem of making a nonchaotic dynamical system chaotic when the system model is unknown. We propose that the unknown system can be identified by using neural networks (e.g. radial basis function neural networks), and then based on the identified model, a statefeedback controller can be designed to make all the Lyapunov exponents of the controlled system strictly positive. The designed controller can drive the unknown system chaotic. Simulations demonstrate the effectiveness of our algorithm.
anticontrol of chaos RBF neural networks state-feedback control
Li Dongmei Wang Zheng-ou
Institute of systems engineering Tianjin University Tianjin, P.R.China, 300072
国际会议
8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)
上海
英文
686-690
2001-11-14（万方平台首次上网日期，不代表论文的发表时间）