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Optimizing the Weights of Neural Networks Based on Antibody Clonal Simulated Annealing Algorithm

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Advances in Neural Networks – ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3173))

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Abstract

Based on the clonal selection theory, a new algorithm, Antibody Clone Simulated Annealing Algorithm, is put forward for optimizing the weights of neural networks. Combining the mechanism of the clonal selection and the simulated annealing, the new algorithm optimizes the weights using a population instead of single point so as to enlarge the searching range and overcome the shortcomings of the simulated annealing algorithm. The effectiveness of the method is proved by the experiments optimizing the weights of the forward neural networks.

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Author information

Authors and Affiliations

  1. Institute for Population and Development Studies, **’an Jiaotong University, 710049, **’an, China

    **aoyi **

  2. Institute of Intelligent Information Processing, **dian University, 710071, **’an, China

    Haifeng Du, Wuhong He & Licheng Jiao

  3. Industry Training Center, **’an Jiaotong University, 710049, **’an, China

    Haifeng Du

Authors
  1. **aoyi **
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  2. Haifeng Du
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  3. Wuhong He
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  4. Licheng Jiao
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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**, X., Du, H., He, W., Jiao, L. (2004). Optimizing the Weights of Neural Networks Based on Antibody Clonal Simulated Annealing Algorithm. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_51

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  • DOI: https://doi.org/10.1007/978-3-540-28647-9_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22841-7

  • Online ISBN: 978-3-540-28647-9

  • eBook Packages: Springer Book Archive

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