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Using Unscented Kalman Filter for Training the Minimal Resource Allocation Neural Network

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Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3610))

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Abstract

The MARN has the same structure as the RBF network and has the ability to grow and prune the hidden neurons to realize a minimal network structure. Several algorithms have been used to training the network. This paper proposes the use of Unscented Kalman Filter (UKF) for training the MRAN parameters i.e. centers, radii and weights of all the hidden neurons. In our simulation, we implemented the MRAN trained with UKF and the MRAN trained with EKF for states estimation. It is shown that the MRAN trained with UKF is superior than the MRAN trained with EKF.

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Author information

Authors and Affiliations

  1. Electronic Information & Engineering Faculty, Nanchang University, Nanchang, 330029, China

    Ye Zhang, Yiqiang Wu & Wenquan Zhang

  2. Computers information engineering college & Engineering Faculty, Jiangxi normal university, Nanchang, 330027, China

    Yi Zheng

Authors
  1. Ye Zhang
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  2. Yiqiang Wu
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  3. Wenquan Zhang
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  4. Yi Zheng
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Zhang, Y., Wu, Y., Zhang, W., Zheng, Y. (2005). Using Unscented Kalman Filter for Training the Minimal Resource Allocation Neural Network. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_2

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  • DOI: https://doi.org/10.1007/11539087_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28323-2

  • Online ISBN: 978-3-540-31853-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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