Abstract
In this paper, the problem on global dissipativity is investigated for stochastic neural networks with time-varying delays and generalized activation functions. By constructing appropriate Lyapunov-Krasovskii functionals and employing stochastic analysis method and linear matrix inequality (LMI) technique, a new delay-dependent criterion for checking the global dissipativity of the addressed neural networks is established in terms of LMIs, which can be checked numerically using the effective LMI toolbox in MATLAB. The proposed dissipativity criterion does not require the monotonicity of the activation functions and the differentiability of the time-varying delays, which means that our result generalizes and further improves those in the earlier publications. An example is given to show the effectiveness and less conservatism of the obtained conditions.
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Zhou, J., Song, Q., Yang, J. (2010). Dissipativity Analysis of Stochastic Neural Networks with Time-Varying Delays. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_79
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DOI: https://doi.org/10.1007/978-3-642-13278-0_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13277-3
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