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An Evolved Recurrent Neural Network and Its Application

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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

An evolved recurrent neural network is proposed which automates the design of the network architecture and the connection weights using a new evolutionary learning algorithm. This new algorithm is based on a cooperative system of evolutionary algorithm (EA) and particle swarm optimisation (PSO), and is thus called REAPSO. In REAPSO, the network architecture is adaptively adjusted by PSO, and then EA is employed to evolve the connection weights with this network architecture, and this process is alternated until the best neural network is accepted or the maximum number of generations has been reached. In addition, the strategy of EAC and ET are proposed to maintain the behavioral link between a parent and its offspring, which improves the efficiency of evolving recurrent neural networks. A recurrent neural network is evolved by REAPSO and applied to the state estimation of the CSTR System. The performance of REAPSO is compared to TDRB, GA, PSO and HGAPSO in these recurrent networks design problems, demonstrating its superiority.

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Authors and Affiliations

  1. Member, IEEE Department of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, 518055, China

    Chunkai Zhang & Hong Hu

Authors
  1. Chunkai Zhang
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  2. Hong Hu
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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, C., Hu, H. (2005). An Evolved Recurrent Neural Network and Its Application. 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_11

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  • DOI: https://doi.org/10.1007/11539087_11

  • 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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