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Modeling of Non-Gaussian AR Model with Transient Coefficients Using Wavelet Basis

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Parallel and Distributed Computing: Applications and Technologies (PDCAT 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3320))

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Abstract

This paper focuses on the modeling of non-Gaussian autoregressive (AR) model for a wide range of physical signals. A practical algorithm based on higher-order cumulants is proposed to deal with the problem of estimating the non-Gaussian AR model with transient coefficients. Wavelet basis is used to identify the transient coefficients. The performance in terms of Haar and Morlet basis is evaluated with non-stationary processes. The experimental results show the flexibility of capturing the local events by using the presented model.

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Author information

Authors and Affiliations

  1. Dept. of Electronic Engineering, Shantou University, Guangdong, 515063, China

    Lisha Sun, Minfen Shen & Weiling Xu

  2. School of System Engineering, Portsmouth University, Portsmouth, U.K.

    Patch Beadle

Authors
  1. Lisha Sun
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  2. Minfen Shen
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  3. Weiling Xu
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  4. Patch Beadle
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Editor information

Editors and Affiliations

  1. Nanyang Centre for Supercomputing and Visualisation School of Mechanical and Production Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639 798, Singapore

    Kim-Meow Liew

  2. School of Computer Science, The University of Adelaide,  

    Hong Shen

  3. Asia Pacific Science and Technology Center, Sun Microsystems Inc., 50 Nanyang Avenue, N3-1c-10, 639798, Singapore

    Simon See

  4. School of Computer Engineering, Nanyang Technological University, 639798, Singapore

    Wentong Cai

  5. Southwest Jiaotong University,  

    Pingzhi Fan

  6. Tohoku University ,  

    Susumu Horiguchi

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© 2004 Springer-Verlag Berlin Heidelberg

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Sun, L., Shen, M., Xu, W., Beadle, P. (2004). Modeling of Non-Gaussian AR Model with Transient Coefficients Using Wavelet Basis. In: Liew, KM., Shen, H., See, S., Cai, W., Fan, P., Horiguchi, S. (eds) Parallel and Distributed Computing: Applications and Technologies. PDCAT 2004. Lecture Notes in Computer Science, vol 3320. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30501-9_155

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  • DOI: https://doi.org/10.1007/978-3-540-30501-9_155

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24013-6

  • Online ISBN: 978-3-540-30501-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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