Synchronization Analysis of Fractional-Order Neural Networks With Adaptive Intermittent-Active Control

Abstract

This paper concentrates on the study of adaptive control for the synchronization of fractional-order neural networks. Instead of classical adaptive control updating method, an intermittent-active updating strategy is proposed to adaptively tune the control gain in a fractional-order fashion. Moreover, quantization is brought into the control design to take into account the restricted bandwidth in signal transmission. Note that the suggested controller is basic yet effective in terms of the fractional-order system. The main theorem is established with the method of reduction to absurdity as well as Lyapunov stability theorem. Finally, simulation calculation is conducted to validate the effectiveness of our proposed method.

New Results on Robust Synchronization for Memristive Neural Networks with Fractional Derivatives via Linear Matrix Inequality

“…In recently years, the dynamical behavior of fractional-order neural networks (FONN) was widely studied in [22][23][24][25][26][27][28][29][30], especially fractional-order memristive neural networks (FOMNNs) [31][32][33][34][35][36][37][38][39][40][41]. Chen et al investigated Mittag-Leffler synchronization of a FOMNN by using an M-matrix method and set-valued theory in [31].…”

Time-Varying Heterogeneous Alternation Control for Synchronization of Delayed Neural Networks

“…But this control technique requires continuous energy use as well. Consequently, discrete control is suggested as a solution to the aforementioned issue, including intermittent control [16]- [18] and impulsive control [7], [19]. It enables the controller to work at a certain discrete instant or for a certain amount of time [20].…”