Guaranteed H Performance State Estimation of Delayed Static Neural Networks

abstract

• This brief studies the guaranteed H performance state estimation problem of delayed static neural networks. The single-and double-integral terms in the time derivative of the Lyapunov functional are handled by the reciprocally convex combination and a new integral inequality, respectively. A delay-dependent design criterion is established such that the error system is globally exponentially stable with a decay rate and a prescribed H performance is guaranteed. The gain matrix and the optimal performance index are obtained via solving a convex optimization problem subject to linear matrix inequalities. A numerical example is exploited to demonstrate that much better performance can be achieved by this approach. 2004-2012 IEEE.

authors

• Huang, Tingwen

published proceedings

• IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS

author list (cited authors)

• Huang, H. e., Huang, T., & Chen, X.

citation count

• 70

complete list of authors

• Huang, He||Huang, Tingwen||Chen, Xiaoping

publication date

• June 2013

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Transactions on Circuits and Systems - II - Express Briefs  Journal

keywords

• Decay Rate
• Guaranteed Performance State Estimation
• Integral Inequality
• Static Neural Networks

Digital Object Identifier (DOI)

• 10.1109/TCSII.2013.2258258

start page

• 371

end page

• 375

volume

• 60

issue

• 6

URL

• http://dx.doi.org/10.1109/tcsii.2013.2258258

user-defined tag

• 10 Reduced Inequalities