Guaranteed $H_{infty}$ 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.

author list (cited authors)

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

citation count

  • 55

publication date

  • May 2013