期刊文献+

基于新蝶状模型的混沌控制及其应用研究 被引量:5

Chaos control and its application based on novel butterfly-shaped model
下载PDF
导出
摘要 针对将混沌技术应用于微弱信号检测的问题,本文提出了一种基于新蝶状(novel butterfly-shaped,NBS)模型的新的混沌控制方法,并将该模型应用于微弱信号检测.首先利用Lyapunov指数谱,结合数值仿真确定系统各个周期态的参数范围,然后根据参数周期微扰法,对扰动参数引入分段控制机制,构建一个受控系统,计算出系统处于特定周期态的参数范围,最后在该范围内选择适当的参数值,即可把系统稳定到所期望的周期轨道上.这种改进策略不需要计算周期激励信号幅值的精确解,大大简化计算的步骤,提高计算效率,控制结构简单,易于实现,且可以应用于微弱呼吸信号的检测.仿真结果表明了该方法的有效性. To apply the chaos technology to weak-signal detection, we propose a new chaotic control method for weak- signal detection based on the Novel butterfly-shaped (NBS) model. Lyapunov exponential spectrum and numerical sim- ulations are adopted to determine the parameter ranges in different periodic system states. According to the perturbation method for periodic parameters, we introduce the segment control mechanism to the perturbed parameters in constructing the controller. The system parameter range is then calculated in a particular periodical state. The appropriate parameter value is selected in this range; thus, the system is stabilized on the expected periodic orbits. This improved strategy needn't calculate the exact solution of the periodic excitation signal's amplitude, reducing the number of calculation steps greatly and increasing the calculation efficiency significantly. In addition, this method is characterized by its simple control struc- ture and easy implementation. It can also be used in the detection of weak breath signal. Simulation results of the NBS system indicate the effectiveness of the proposed method.
出处 《控制理论与应用》 EI CAS CSCD 北大核心 2012年第7期915-920,共6页 Control Theory & Applications
基金 国家自然科学基金资助项目(50875070)
关键词 NBS模型 LYAPUNOV指数 分段控制 信号检测 NBS model Lyapunov exponent segment control signal detection
  • 相关文献

参考文献12

二级参考文献77

共引文献178

同被引文献32

  • 1李月,杨宝俊,林红波,刘晓华.基于特定混沌系统微弱谐波信号频率检测的理论分析与仿真[J].物理学报,2005,54(5):1994-1999. 被引量:37
  • 2ZHAO H T, LIN Y P, DAI Y X. Bifurcation analysis and control of chaos for a hybrid ratio-dependent three species food chain [J]. Ap- plied Mathematics and Computation, 2011, 218(5): 1533 - 1546.
  • 3YI N, LIU P, ZHANG Q L. Bifurcations analysis and tracking con- trol of an epidemic model with nonlinear incidence rate [J]. Applied Mathematical Modelling, 2012, 36(4): 1678 1693.
  • 4LE H N, HONG K S. Hopf bifurcation control via a dynamic state- feedback control [J]. Physics Letters A, 2012, 376(4): 442 - 446.
  • 5SEBASTIAN SUDHEER K, SABIR M. Switched modified function projective synchronization of hyperchaotic Qi system with uncertain parameters [J]. Communications in Nonlinear Science and Numerical Simulation, 2010, 15 (12): 4058 - 4064.
  • 6ZHANG R Y, SUN G L. Hopf bifurcation control of the Qi 3-D four wing chaotic system via washout filter[C] //The 2nd Interna- tional Conference on Intelligent Control and Information Processing. Harbin: IEEE, 2011, 1:177 - 179.
  • 7HASSARD B D. Theory and Application of Hopf Bifitrcation [ M]. New York: Cambridge University, 1981.
  • 8Birx D I. Chaotic oscillators and CMFFNS for signal detection in noise environments[J]. IEEE International Joint Conference on Neural Netsorks, 1992: 881-888.
  • 9CHOE C U, HOHNE K, BENNER H, et al. Chaos suppression in the parametrically driven Lorenz system[J]. Physical Review E, 2005, 72: 036206.
  • 10Hubler A.Adaptive control of chaotic systems[J].Helv.Phys.Acta,1989,62(9):343-356.

引证文献5

二级引证文献15

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部