H∞ stabilization problem for memristive neural networks with time-varying delays

This work explores memristive neural networks' (MNNs) stability and stabilization problems by considering the time-varying delay and external disturbance. First, the MNNs were transformed into a tractable model by defining the logical switched functions, paving the way to utilize the robust analysis method for the associated connection weights. Second, by proposing a new Lyapunov-Krasovskii functional constructed by the delay-partitioning approach and the free weight matrices, the exponential stability (ES) problem of the transformed MNNs model is investigated. Third, the state feedback controller's design scheme is devised to ensure the ES of the overall closed-loop system with a prescribed H1 disturbance attenuation performance level gamma. The results are formed in terms of linear matrix inequalities. Finally, two suitable examples express the efficacy of the proposed results. (C) 2022 Elsevier Inc. All rights reserved.

Automated summary

This work investigates the stability and stabilization problems of memristive neural networks (MNNs) considering time-varying delay and external disturbance. The MNNs are transformed into a tractable model by defining logical switched functions. A new Lyapunov-Krasovskii functional is proposed to study the exponential stability (ES) problem of the transformed MNNs model. The design scheme of a state feedback controller is devised to ensure the stability of the overall closed-loop system. The efficacy of the proposed results is demonstrated through suitable examples.