Further Result on Guaranteed <inline-formula> <tex-math notation="LaTeX">$H_\infty $ </tex-math></inline-formula> Performance State Estimation of Delayed Static Neural Networks

Abstract

This brief considers the guaranteed H∞ performance state estimation problem of delayed static neural networks. An Arcak-type state estimator, which is more general than the widely adopted Luenberger-type one, is chosen to tackle this issue. A delay-dependent criterion is derived under which the estimation error system is globally asymptotically stable with a prescribed H∞ performance. It is shown that the design of suitable gain matrices and the optimal performance index are accomplished by solving a convex optimization problem subject to two linear matrix inequalities. Compared with some previous results, much better performance is achieved by our approach, which is greatly benefited from introducing an additional gain matrix in the domain of activation function. An example is finally given to demonstrate the advantage of the developed result.