Improved Generalized H2 Filtering for Static Neural Networks with Time-Varying Delay via Free-Matrix-Based Integral Inequality
2018 ◽
Vol 2018
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pp. 1-9
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This paper focuses on the generalized H2 filtering of static neural networks with a time-varying delay. The aim of this problem is to design a full-order filter such that the filtering error system is globally asymptotically stable with guaranteed H2 performance index. By constructing an augmented Lyapunov-Krasovskii functional and applying the free-matrix-based integral inequality to estimate its derivative, an improved delay-dependent condition for the generalized H2 filtering problem is established in terms of LMIs. Finally, a numerical example is presented to show the effectiveness of the proposed method.
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2020 ◽
Vol 9
(6)
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pp. 3441-3451
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2011 ◽
Vol 41
(6)
◽
pp. 1522-1530
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2011 ◽
Vol 217
(24)
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pp. 10278-10288
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Keyword(s):
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