Research and Exploration of Volterra Series on Nonlinear System Identification and Modeling

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Abstract:

With the development of analysis and identification way to nonlinear dynamic system, people uses many different method to build up mathematics model to simulate nonlinear dynamic system. This paper introduces some important nonlinear system identification ways and a kind of Volterra series expression type in detail. This kind of way adopts Hilbert reproducing kernel method to build up nonlinear dynamic system model. Hilbert space provides a kind of effective expression type for Fourier series and transfer based on anyorthogonal polynomial. Volterra series function has very strict theory basic, which can be applied into many nonlinear dynamic system analysis and identification filed, and has broad practicality and application prospect.

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Key Engineering Materials (Volumes 439-440)

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584-589

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June 2010

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© 2010 Trans Tech Publications Ltd. All Rights Reserved

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[1] Jiao Licheng. Nonlinear transfer function theory and application, Xi'an: Xidian University Publishing House. (1992).

Google Scholar

[2] Cao Jianfu, Han Zongshao, Fang Yangwang. Nonlinear system theory and application, Xi'an: Xian Jiaotong University Publishing House. (2006).

Google Scholar

[3] Leontaritis I J, Billings S A. Input-output parametric models for non-linear systems. Int.J. Control. (1985), pp.303-344.

DOI: 10.1080/0020718508961130

Google Scholar

[4] Mathews V J. Adaptive polynomial filters. IEEE Signal Proc Magazine. (1991), pp.10-26.

Google Scholar

[5] GRIFFITH D W, ARCE G R, Partially decoupled Volterra filters:formulation and LMS adaptation, IEEE Trans Signal Processing. (1997), pp.1485-1494.

DOI: 10.1109/78.599973

Google Scholar

[6] NOWAK R D, VAN VEEN B D, Random and pseudorandom inputs for Volterra system identification, IEEE Trans. Signal Processing. (1994), pp.2124-2135.

DOI: 10.1109/78.301847

Google Scholar

[7] Wei Ruixuan, Han Chongzhao. A Staged Adaptive Identification Method with Fully Decoupled Structure for Nonlinear System Based on Volterra Model, Control theory and applications. (2002), pp.313-315.

DOI: 10.1109/wcica.2004.1340580

Google Scholar

[8] Aronszajn N, Theory of reproducing kernels, Transactions of the American Mathematical Society. (1950). pp.337-404.

DOI: 10.1090/s0002-9947-1950-0051437-7

Google Scholar