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Dissipativity Analysis of a Class of Competitive Neural Networks with Proportional Delays

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Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation (ICANN 2019)

Abstract

This paper dealt with the dissipativity problem for a class of competitive neural networks with proportional delays. Based on Lyapunov functionals approach, new sufficient conditions are derived to ensuring the strictly \((Q,\; S^{*},\; R)-\)dissipative of the model. The conditions are presented in terms of linear matrix inequalities (LMIs) and can be easily numerically checked by the MATLAB LMI toolbox. At last, a numerical example with simulation is given to illustrate the validity of the obtained theoretical results.

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Correspondence to Chaouki Aouiti .

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Aouiti, C., Chérif, F., Touati, F. (2019). Dissipativity Analysis of a Class of Competitive Neural Networks with Proportional Delays. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. ICANN 2019. Lecture Notes in Computer Science(), vol 11727. Springer, Cham. https://doi.org/10.1007/978-3-030-30487-4_3

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  • DOI: https://doi.org/10.1007/978-3-030-30487-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30486-7

  • Online ISBN: 978-3-030-30487-4

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