Finite-horizon H∞ state estimation for artificial neural networks with component-based distributed delays and stochastic protocol

Abstract

Abstract This paper is concerned with the H ∞ state estimation problem for time-varying artificial neural networks with component-based distributed delays and stochastic protocol scheduling. A shared communication channel is adopted for data transmissions between the sensors and the estimator. For the purpose of avoiding data collisions, the stochastic protocol is used to schedule the transmission opportunities of sensors. A finite-horizon H ∞ index is introduced to reflect the performance specification of the estimation. The aim of this paper is to design a time-varying H ∞ estimator over a given finite-horizon such that the dynamics of the estimation error satisfy the given H ∞ performance requirement. Sufficient conditions are established for the existence of the desired estimator and the explicit expressions of the desired estimator parameters are then given in terms of the solutions to a set of recursive linear matrix inequalities. Finally, a numerical example is given to demonstrate the effectiveness of the developed state estimation scheme.