The Non-Linear Chaotic Model Reconstruction for the Exerimental Data Obtained From Different Dynamic System

Ma Junhai; Chen Yushu; Liu Zengrong

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Article Navigation > Applied Mathematics and Mechanics > 1999 > 20(11): 1128-1134

Ma Junhai, Chen Yushu, Liu Zengrong. The Non-Linear Chaotic Model Reconstruction for the Exerimental Data Obtained From Different Dynamic System[J]. Applied Mathematics and Mechanics, 1999, 20(11): 1128-1134.

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The Non-Linear Chaotic Model Reconstruction for the Exerimental Data Obtained From Different Dynamic System

1.
Department of Economy and Management, Tianjin Finance University, Tianjin 300222, P. R. China;
2.
Department of Mechanics, Tianjin University, Tianjin 300072, P. R. China;
3.
Department of Mathematics, Shanghai University, Shanghai 201800, P. R. China

Received Date: 1998-06-29
Rev Recd Date: 1999-05-08
Publish Date: 1999-11-15

Abstract

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. Then 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.
- non-linear,
- chaotic timeseries,
- Lyapunov exponent,
- chaotic model,
- parameter identification

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References(13)

References

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