Journal
NEUROCOMPUTING
Volume 74, Issue 4, Pages 606-616Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2010.09.017
Keywords
Static neural networks; State estimation; Performance analysis; Time-varying delay; Convex optimization
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Funding
- National Natural Science Foundation of China [61005047, 11072059]
- Natural Science Foundation of Jiangsu Province of China [BK2010214, BK2009271]
- Specialized Research Fund for the Doctoral Program of Higher Education [20070286003]
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This paper is concerned with studying two kinds of guaranteed performance state estimation problems for static neural networks with time-varying delay. Both delay-independent and delay-dependent design criteria are presented under which the resulting estimation error system is globally asymptotically stable and a prescribed performance is guaranteed in the H(infinity) or generalized H(2) sense. It is shown that the gain matrices of the state estimator and the optimal performance indexes can be simultaneously obtained by solving convex optimization problems subject to linear matrix inequalities. It is worth noting that no slack variable is introduced in the proposed conditions, and thus the computational burden is reduced. The effectiveness of the developed results is finally demonstrated by simulation examples. (c) 2010 Elsevier B.V. All rights reserved.
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