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Title of Journal: Neural Comput Applic

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Abbravation: Neural Computing and Applications

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Springer London

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DOI

10.1007/s11684-010-0002-0

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ISSN

1433-3058

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Delaydependent Emphasis Type="Italic"H/Emphasi

Authors: Mingang Hua Huasheng Tan Junfeng Chen
Publish Date: 2013/12/22
Volume: 25, Issue: 3-4, Pages: 613-624
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Abstract

This paper presents the delaydependent H infty and generalized H 2 filters design for stochastic neural networks with timevarying delay and noise disturbance The stochastic neural networks under consideration are subject to timevarying delay in both the state and measurement equations The aim is to design a stable fullorder linear filter assuring asymptotical meansquare stability and a prescribed H infty or generalized H 2 performance indexes for the filtering error systems Delaydependent sufficient conditions for the existence of H infty and generalized H 2 filters are both proposed in terms of linear matrix inequalities Finally numerical example demonstrates that the proposed approaches are effective

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