Exponential stabilisation of stochastic memristive neural networks under intermittent adaptive control

• Published in: IET control theory & applications Vol. 11; no. 15; pp. 2432 - 2439
• Main Authors: Li, Xiaofan, Fang, Jian-an, Li, Huiyuan
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 13.10.2017
• Subjects: adaptive control; adaptive control technique; adaptive updated rule; asymptotic stability; Brownian motion; Brownian motions; control system synthesis; differential inclusion theory; exponential stabilisation problem; functional equations; Lyapunov methods; Lyapunov‐Krasovskii functional; neurocontrollers; periodic control; periodically intermittent adaptive control strategy design; Research Article; set‐valued maps; stochastic disturbance; stochastic disturbances; stochastic memristive neural networks; stochastic systems; sufficient conditions; time‐varying delays; time‐varying systems; time-varying systems; stochastic disturbances; periodically intermittent adaptive control strategy design; differential inclusion theory; adaptive control; control system synthesis; exponential stabilisation problem; periodic control; asymptotic stability; stochastic memristive neural networks; Brownian motion; sufficient conditions; functional equations; neurocontrollers; Brownian motions; stochastic systems; set-valued maps; Lyapunov-Krasovskii functional; stochastic disturbance; adaptive updated rule; time-varying delays; adaptive control technique; Lyapunov methods
• ISSN: 1751-8644; 1751-8652; 1751-8652
• DOI: 10.1049/iet-cta.2017.0021

This study focuses on the exponential stabilisation problem for a general class of memristive neural networks subjected to both stochastic disturbance and time-varying delays under periodically intermittent adaptive control. The stochastic disturbances are described as Brownian motions in the considered networks. An adaptive updated rule and a periodically intermittent adaptive control strategy are designed for the exponential stabilisation of memristive neural networks subjected to both stochastic disturbance and time-varying delays. Then, by adopting the adaptive control technique, differential inclusion theory and set-valued maps, and by building a new Lyapunov–Krasovskii functional, many novel sufficient conditions are proposed to guarantee exponential stabilisation for stochastic memristive neural networks. Different from existing results on stabilisation of stochastic memristive neural networks, the obtained criteria in this study are directly derived according to the parameters of networks. Finally, an example is carried out to demonstrate the validity of the theoretic results.