Adaptive Synchronization of Fractional-Order Output-Coupling Neural Networks via Quantized Output Control

Abstract

This article focuses on the adaptive synchronization for a class of fractional-order coupled neural networks (FCNNs) with output coupling. The model is new for output coupling item in the FCNNs that treat FCNNs with state coupling as its particular case. Novel adaptive output controllers with logarithm quantization are designed to cope with the stability of the fractional-order error systems for the first attempt, which is also an effective way to synchronize fractional-order complex networks. Based on fractional-order Lyapunov functionals and linear matrix inequalities (LMIs) method, sufficient conditions rather than algebraic conditions are built to realize the synchronization of FCNNs with output coupling. A numerical simulation is put forward to substantiate the applicability of our results.

Dynamic optimal operational control for complex systems with uncertain external loop disturbances

“…PID controllers are famous for their simple structure and easy implement 18‐25 . In this article, the following PID controller () is also adopted in the loop control level: The difference is the PID parameters K P , K I , K D are not based on experience, but are determined by strict LMI algorithms 26‐29 …”

Quantised control for local Mittag–Leffler stabilisation of fractional‐order neural networks with input saturation: A refined sector condition

“…It is reasonable and necessary to improve the efficiency of information processing and the accuracy of the model by introducing fractional capacitors in the large‐scale integrated circuits of fractional‐order neural networks [5]. Accordingly, considerable attention has been paid to FNNs and many outstanding results, such as synchronisation, have been developed [8–12]. It is well known that stability is a prerequisite for ensuring neural networks steadily work [13–16].…”

“…As information and communications technology have grown, networked control methods, such as sampled‐data control [21, 22], event‐triggered control [23–25], intermittent control [12, 26], and quantised control [10, 27–29], have become a popular research topic in engineering applications. The common feature of these networked control methods is that the control information is digital and discrete.…”

“…Hence, it is an energy‐saving method to release the congested I/O ports of CPU. For instance, the synchronisation problem has been investigated for fractional‐order coupled neural networks by using novel adaptive quantised output controllers [10]. In addition, the mean‐square σ‐error stable problem has been discussed for semi‐Markov jump systems under quantised output feedback control [28].…”

“…In addition, the mean‐square σ‐error stable problem has been discussed for semi‐Markov jump systems under quantised output feedback control [28]. However, most relevant results, except [10], are mainly focused on integer‐order systems since the classical Lyapunov methods and inequities analysis techniques cannot be used for fractional‐order systems. It is, therefore, a natural and necessary idea to investigate the stabilisation of FNNs under quantised control.…”