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Stability of Nonnegative Periodic Solutions of High-Ordered Neural Networks

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Advances in Neural Networks – ISNN 2013 (ISNN 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7951))

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Abstract

In this paper, a class of high-ordered neural networks are investigated. By rigorous analysis, a set of sufficient conditions ensuring the existence of a nonnegative periodic solution and its \(R^n_+\)-asymptotical stability are established. The results obtained can also be applied to the first-ordered neural networks.

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Author information

Authors and Affiliations

  1. Department of Applied Mathematics, Shanghai University of Finance and Economics, Shanghai, P.R. China

    Lili Wang

  2. School of Computer Sciences, Key Laboratory of Nonlinear Mathematics Science, School of Mathematical Sciences, Fudan University, Shanghai, P.R. China

    Tianping Chen

Authors
  1. Lili Wang
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  2. Tianping Chen
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Editor information

Editors and Affiliations

  1. School of Information and Communication Engineering, Dalian University of Technology, A 530, Chuangxinyuan Building, 116023, Dalian, China

    Chengan Guo

  2. Institute of Automation, Chinese Academy of Sciences, 100864, Beijing, China

    Zeng-Guang Hou

  3. School of Automation, Huazhong University of Science and Technology, 430074, Wuhan, China

    Zhigang Zeng

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Wang, L., Chen, T. (2013). Stability of Nonnegative Periodic Solutions of High-Ordered Neural Networks. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39065-4_22

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  • DOI: https://doi.org/10.1007/978-3-642-39065-4_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39064-7

  • Online ISBN: 978-3-642-39065-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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