Exponential extended dissipative performance for delayed discrete-time neural networks under memoryless resilient-based observer design

This paper is concerned with the problem of exponentially extended dissipative criteria for a class of delayed discrete-time neural networks (DNNs) subject to resilient observer-based controller design. For this objective, a memoryless full-order Luenberger state observer is designed, and further, its observer error system is calculated with resilient control. Initially, some new improved weighted summation inequalities are proposed by combining weighted summation inequality and an extended reciprocal convex matrix inequality. By constructing the suitable Lyapunov-Krasovskii functional (LKF) and utilizing the developed summation inequalities, the exponentially extended dissipative criterion is obtained for the considered delayed DNNs. The designed observer and resilient control gain matrices can be determined by solving a set of linear matrix inequalities (LMIs) subject to the prescribed exponential decay rate. Finally, two numerical examples are carried out to illustrate the feasibility and effectiveness of the established theoretical results obtained through the newly developed summation inequalities. (c) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper addresses the problem of exponentially extended dissipative criteria for a class of delayed discrete-time neural networks (DNNs) with resilient observer-based controller design. By designing a memoryless full-order Luenberger state observer and resilient control, the exponentially extended dissipative criterion is obtained and the observer and resilient control gain matrices are determined by solving a set of linear matrix inequalities (LMIs).