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A New Learning Algorithm for Diagonal Recurrent 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

A new hybrid learning algorithm combining the extended Kalman filter (EKF) and particle filter is presented. The new algorithm is firstly applied to train diagonal recurrent neural network (DRNN). The EKF is used to train DRNN and particle filter applies the resampling algorithm to optimize the particles, namely DRNNs, with the relative network weights. These methods make the training shorter and DRNN convergent more quickly. Simulation results of the nonlinear dynamical identification verify the validity of the new algorithm.

This work is supported by the National Natural Science Foundation of China, No. 50405017.

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Author information

Authors and Affiliations

  1. Department of Automation, Shanghai Jiaotong University, 200030, Shanghai, China

    Deng Xiaolong & Xie Jianying

  2. Department of Mechanical Engineering, Shanghai Jiaotong University, 200030, Shanghai

    Guo Weizhong

  3. First Research Institute of Corps of Engineers, General Armaments Department, PLA, 214035, Wuxi, Jiangsu, China

    Liu Jun

Authors
  1. Deng Xiaolong
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  2. Xie Jianying
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  3. Guo Weizhong
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  4. Liu Jun
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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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Xiaolong, D., Jianying, X., Weizhong, G., Jun, L. (2005). A New Learning Algorithm for Diagonal Recurrent 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_6

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  • DOI: https://doi.org/10.1007/11539087_6

  • 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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