H∞ Filtering for Discrete-Time Neural Networks System with Time- Varying Delay and Sensor Nonlinearities

ABSTRACT

The H∞ filtering problem for a class of discrete stochastic neural networks systems with time-varying delay and nonlinear sensor is investigated. By employing the Lyapunov stability theory and linear matrix inequality optimization approach, sufficient conditions to guarantee the filtering error systems asymptotically stable are provided. By setting on the lower and upper bounds of the discrete time-varying delays, an acceptable state-space realization of the H∞ and an acceptable H∞ performance index are obtained in terms of linear matrix inequality (LMI). Numerical examples and simulations are provided to illustrate the effectiveness of the proposed methods.

Keywords:

Asymptotically stable, discrete stochastic neural networks systems, linear matrix inequality (LMI), Sensor Nonlinearities, time-varying delay.