Guaranteed H-infinity Performance State Estimation of Delayed Static Neural Networks Academic Article uri icon

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.

published proceedings

  • IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS

author list (cited authors)

  • Huang, H. e., Huang, T., & Chen, X.

citation count

  • 67

complete list of authors

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

publication date

  • May 2013