In this paper, the problem on global dissipativity is investigated for neural networks with time-varying delays and leakage delay as well as generalized activation functions. By constructing appropriate Lyapunov-Krasovskii functionals and using 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. © 2012 Springer-Verlag.
CITATION STYLE
Zhao, Z., & Song, Q. (2012). Global dissipativity of neural networks with time-varying delay and leakage delay. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7367 LNCS, pp. 328–335). https://doi.org/10.1007/978-3-642-31346-2_37
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