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This paper focuses on the generalized 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 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 filtering problem is established in terms of LMIs. Finally, a numerical example is presented to show the effectiveness of the proposed method.

mathematical problems in engineering

Improved Generalized <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mn fontstyle="italic">2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math> Filtering for Static Neural Networks with Time-Varying Delay via Free-Matrix-Based Integral Inequality

Improved Generalized H2 Filtering for Static Neural Networks with Time-Varying Delay via Free-Matrix-Based Integral Inequality