摘要
本文采用 Wiener模型来辨识混沌系统 ,利用扩展 Kalm an滤波算法加快辨识过程 .在此基础上 ,设计非线性广义预测控制算法对混沌系统加以控制 ,引入增量因子改善了动态过渡过程特性 .大量的辨识和控制仿真验证了本文的有效性 .
An identification structure based on Wiener model is presented for chaotic system, and an extended kalman filter algorithm is used to quicken neural training. A novel nonlinear general predictive control paradigm is developed to control chaos, and a incremental gene is introduced to improve dynamic system performance. Simulation studies verify effectiveness of our proposed identification and control structure.
出处
《信息与控制》
CSCD
北大核心
2001年第3期227-230,共4页
Information and Control
基金
国家 8 63 /CIMS基金!( 863 -5 11-945 -0 10 )
教育部骨干教师基金资助
