Abstract
This paper focuses on the exponential stabilization problem for Markov jump neural networks with Time-varying Delays (TDs). Firstly, we provide a new Free-matrix-based Exponential-type Integral Inequality (FMEII) containing the information of attenuation exponent, which is helpful to reduce the conservativeness of stability criteria. To further save control cost, we introduce a sample-based Adaptive Event-triggered Impulsive Control (AEIC) scheme, in which the trigger threshold is adaptively varied with the sampled state. By fully considering the information about sampled state, TDs, and Markov jump parameters, a suitable Lyapunov–Krasovskii functional is constructed. With the virtue of FMEII and AEIC scheme, some novel stabilization criteria are presented in the form of linear matrix inequalities. At last, two numerical examples are given to show the validity of the obtained results.
Highlights
Recent decades have witnessed the fast development of neural networks since its wide applications in many practical fields, such as pattern recognition [1], smart antenna arrays [2], and circuit design [3]
In the implementation of such applications, both Time-varying Delays (TDs) caused by the inherent communication time among neurons or the finite switching speed of amplifier [4,5,6,7,8] and random abrupt arisen from the external environment sudden change or the information latching [9,10,11] are inevitably encountered, which often lead to some undesirable dynamic behaviors, such as chaotic, oscillation, and even unstable [12,13,14]
Markov Jump Neural Networks (MJNNs) with TDs, and many fruit results have been reported in the literature [16,17,18]
Summary
Recent decades have witnessed the fast development of neural networks since its wide applications in many practical fields, such as pattern recognition [1], smart antenna arrays [2], and circuit design [3]. Markov Jump Neural Networks (MJNNs) with Time-varying Delays (TDs), as a special kind of hybrid system, has a powerful ability in describing those complicated behaviors. Stability is a precondition for the normal operation of systems, and sometimes fast convergence of the networks is essential for real-time computation. As it is well known, the exponential convergence rate is generally used to determine the speed of neural computations [15]. Under an assumption that the proportional delay is unbounded time-varying, some global stochastic exponential stability conditions for MJNNs with proportional TDs are derived in [16]. By considering a more general uncertain transition rates, Liu et al [17] investigated the stochastic exponential stability for neutral-type impulsive
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
