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Applied Mathematics and Mechanics

, Volume 20, Issue 11, pp 1214–1221 | Cite as

The non-linear chaotic model reconstruction for the experimental data obtained from different dynamic system

  • Ma Juhai
  • Chen Yushu
  • Liu Zengrong
Article

Abstract

The non-linear chaotic model reconstruction is the major important quantitative index for describing accurate experimental data obtained in dynamic analysis. A lot of work has been done to distinguish chaos from randomness, to calulate fractral dimension and Lyapunov exponent, to reconstruct the state space and to fix the rank of model. In this paper, a new improved EAR method is presented in modelling and predicting chaotic timeseries, and a successful approach to fast estimation algorithms is proposed. Some illustrative experimental data examples from known chaotic systems are presented, emphasising the increase in predicting error with time. The calculating results tell us that the parameter identification method in this paper can effectively adjust the initial value towards the global limit value of the single peak target function nearby. The the model paremeter can immediately be obtained by using the improved optimization method rapidly, and non-linear chaotic models can not provide long period superior predictions. Applications of this method are listed to real data from widely different areas.

Key words

non-linear chaotic timeseries Lyapunov exponent chaotic model parameter identification CLC number O175.14 O241.81 

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Copyright information

© Editorial Committee of Applied Mathematics and Mechanics 1999

Authors and Affiliations

  • Ma Juhai
    • 1
  • Chen Yushu
    • 2
  • Liu Zengrong
    • 3
  1. 1.Department of Economy and ManagementTianjin Finance UniversityTianjinP R China
  2. 2.Department of MechanicsTianjin UniversityTranjinP R China
  3. 3.Department of MathematicsShanghai UniversityShanghaiP R China

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