Song, Qiankun; Wang, Zidong New results on passivity analysis of uncertain neural networks with time-varying delays. (English) Zbl 1186.68392 Int. J. Comput. Math. 87, No. 3, 668-678 (2010). Summary: The passivity problem is investigated for a class of uncertain neural networks with generalized activation functions. By employing an appropriate Lyapunov-Krasovskii functional, a new delay-dependent criterion for the passivity of the addressed neural networks is established in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. An example is given to show the effectiveness and less conservatism of the proposed criterion. It is noteworthy that the traditional assumptions on the differentiability of the time-varying delays and the boundedness of its derivative are removed. Cited in 18 Documents MSC: 68T05 Learning and adaptive systems in artificial intelligence 15A06 Linear equations (linear algebraic aspects) 15A39 Linear inequalities of matrices 37Fxx Dynamical systems over complex numbers 37Nxx Applications of dynamical systems Keywords:uncertain neural networks; time-varying delays; passivity; Lyapunov-Krasovskii functional; linear matrix inequality Software:LMI toolbox; Matlab PDF BibTeX XML Cite \textit{Q. Song} and \textit{Z. Wang}, Int. J. Comput. Math. 87, No. 3, 668--678 (2010; Zbl 1186.68392) Full Text: DOI References: [1] DOI: 10.1016/j.neunet.2004.02.001 · Zbl 1068.68118 · doi:10.1016/j.neunet.2004.02.001 [2] Bevelevich V., Classical Network Synthesis (1968) [3] DOI: 10.1016/S0005-1098(97)00202-1 · Zbl 0919.93047 · doi:10.1016/S0005-1098(97)00202-1 [4] DOI: 10.1109/TNN.2006.881488 · doi:10.1109/TNN.2006.881488 [5] DOI: 10.1016/j.na.2006.02.009 · Zbl 1120.34055 · doi:10.1016/j.na.2006.02.009 [6] DOI: 10.1109/81.739186 · Zbl 0948.92002 · doi:10.1109/81.739186 [7] DOI: 10.1016/S0005-1098(96)00180-X · Zbl 0883.93045 · doi:10.1016/S0005-1098(96)00180-X [8] DOI: 10.1137/060655110 · Zbl 1140.93425 · doi:10.1137/060655110 [9] DOI: 10.1109/TNN.2005.860874 · doi:10.1109/TNN.2005.860874 [10] DOI: 10.1016/0005-1098(77)90020-6 · Zbl 0356.93025 · doi:10.1016/0005-1098(77)90020-6 [11] DOI: 10.1109/TCSII.2005.849023 · doi:10.1109/TCSII.2005.849023 [12] DOI: 10.1016/j.camwa.2006.03.029 · Zbl 1122.93368 · doi:10.1016/j.camwa.2006.03.029 [13] DOI: 10.1007/s11071-007-9303-5 · Zbl 1172.92002 · doi:10.1007/s11071-007-9303-5 [14] DOI: 10.1016/j.neunet.2005.03.015 · Zbl 1102.68569 · doi:10.1016/j.neunet.2005.03.015 [15] DOI: 10.1016/j.physleta.2008.02.085 · Zbl 1220.90040 · doi:10.1016/j.physleta.2008.02.085 [16] DOI: 10.1016/j.neucom.2007.02.003 · doi:10.1016/j.neucom.2007.02.003 [17] DOI: 10.1016/j.jmaa.2003.11.055 · Zbl 1084.93014 · doi:10.1016/j.jmaa.2003.11.055 [18] DOI: 10.1109/TNN.2007.912593 · doi:10.1109/TNN.2007.912593 [19] DOI: 10.1109/TSMCB.2007.913124 · doi:10.1109/TSMCB.2007.913124 [20] DOI: 10.1109/9.911424 · Zbl 1056.93610 · doi:10.1109/9.911424 [21] DOI: 10.1016/j.chaos.2005.04.124 · Zbl 1152.34380 · doi:10.1016/j.chaos.2005.04.124 [22] DOI: 10.1109/TCSII.2006.886464 · doi:10.1109/TCSII.2006.886464 [23] DOI: 10.1016/j.physleta.2005.07.025 · Zbl 1345.92017 · doi:10.1016/j.physleta.2005.07.025 [24] DOI: 10.1016/j.physleta.2006.01.061 · Zbl 1181.93068 · doi:10.1016/j.physleta.2006.01.061 [25] DOI: 10.1109/TNN.2006.872355 · doi:10.1109/TNN.2006.872355 [26] DOI: 10.1109/81.956024 · Zbl 0999.94577 · doi:10.1109/81.956024 [27] DOI: 10.1109/78.709527 · doi:10.1109/78.709527 [28] DOI: 10.1109/TCSI.2007.890604 · Zbl 1374.34289 · doi:10.1109/TCSI.2007.890604 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.