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International Symposium on Neural Networks

ISNN 2005: Advances in Neural Networks – ISNN 2005 pp 247–252Cite as

Convergence Analysis of Genetic Regulatory Networks Based on Nonlinear Measures

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3496))

Abstract

In this paper, we propose a nonlinear 2-measure concept, and using it, together with the nonlinear 1-measure proposed earlier by other researchers, to analyze the global convergence of genetic regulatory networks. Two sufficient conditions are derived to ensure globally exponentially convergence of solutions of the networks. The derived conditions provide insight into the dynamical behavior of gene networks.

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References

  1. Smolen, P., D.A.B., Byrne, J.H.: Mathematical Modelling of Gene Networks Review. Neuron 26, 567–580 (2000)

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China

    Hongtao Lu

  2. Bio-X Life Science Research Center, Shanghai Jiao Tong University, Shanghai, 200030, China

    Zhizhou Zhang & Lin He

Authors
  1. Hongtao Lu
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  2. Zhizhou Zhang
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  3. Lin He
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Editor information

Editors and Affiliations

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

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiaofeng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Cite this paper

Lu, H., Zhang, Z., He, L. (2005). Convergence Analysis of Genetic Regulatory Networks Based on Nonlinear Measures. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_38

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  • DOI: https://doi.org/10.1007/11427391_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25912-1

  • Online ISBN: 978-3-540-32065-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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