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Global Exponential Stability for Matrix-Valued Neural Networks with Time Delay

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Book cover Advances in Neural Networks - ISNN 2017 (ISNN 2017)

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Abstract

Complex-, quaternion-, and Clifford-valued neural networks can all be generalized to matrix-valued neural networks, which have matrix states. This paper derives a sufficient criterion given in the form of linear matrix inequalities that guarantees the global exponential stability of the equilibrium point for matrix-valued Hopfield neural networks with time delay. A simulation example demonstrates the effectiveness of the theoretical results.

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Author information

Authors and Affiliations

  1. Department of Computer and Software Engineering, Polytechnic University Timişoara, Blvd. V. Pârvan, No. 2, 300223, Timişoara, Romania

    Călin-Adrian Popa

Authors
  1. Călin-Adrian Popa
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Correspondence to Călin-Adrian Popa .

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Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fengyu Cong

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Andrew Leung

  3. Chinese Academy of Sciences, Beijing, China

    Qinglai Wei

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Popa, CA. (2017). Global Exponential Stability for Matrix-Valued Neural Networks with Time Delay. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_51

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  • DOI: https://doi.org/10.1007/978-3-319-59072-1_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59071-4

  • Online ISBN: 978-3-319-59072-1

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