Local Projection Noise Reduction Based on Nonlinear Constraints

Han Min; Liu Yun-xia

doi:10.3724/SP.J.1146.2007.01330

Volume 31 Issue 2

Dec. 2010

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Han Min, Liu Yun-xia. Local Projection Noise Reduction Based on Nonlinear Constraints[J]. Journal of Electronics & Information Technology, 2009, 31(2): 400-404. doi: 10.3724/SP.J.1146.2007.01330

Citation:

Han Min, Liu Yun-xia. Local Projection Noise Reduction Based on Nonlinear Constraints[J]. Journal of Electronics & Information Technology, 2009, 31(2): 400-404. doi: 10.3724/SP.J.1146.2007.01330

Han Min, Liu Yun-xia. Local Projection Noise Reduction Based on Nonlinear Constraints[J]. Journal of Electronics & Information Technology, 2009, 31(2): 400-404. doi: 10.3724/SP.J.1146.2007.01330

Citation:

Han Min, Liu Yun-xia. Local Projection Noise Reduction Based on Nonlinear Constraints[J]. Journal of Electronics & Information Technology, 2009, 31(2): 400-404. doi: 10.3724/SP.J.1146.2007.01330

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Local Projection Noise Reduction Based on Nonlinear Constraints

doi: 10.3724/SP.J.1146.2007.01330

Received Date: 2007-08-16
Rev Recd Date: 2008-01-21
Publish Date: 2009-02-19

Abstract

Abstract

An improved method is proposed for noise reduction of chaotic time series based on the reconstruction of phase space theory. Recursive map is firstly used for the chaos characteristics analysis of the time series observed, then the conditions of nonlinear constraints are introduced to the local projection method, and Singular Spectrum Analysis(SSA) is combined in the local neighborhood, which uses the main components representing the attractors to reconstruct the time series. The improved method raised in this paper overcomes the problems that the traditional local projection can not fully character the nonlinear relationship of system, reduces the deviation of the reconstruction, and improves the signal-to-noise ratio of the system. The chaotic time series generated by Lorenz model and sunspot time series are respectively applied to simulation analysis. The numerical experiment results confirm the effect of the method raised in this paper for noise reduction in the time series observed.

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References(1)

References

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