Synchronization of unknown chaotic delayed competitive neural networks with different time scales based on adaptive control and parameter identification

Abstract

In this paper, we investigate the synchronization problems of delayed competitive neural networks with different time scales and unknown parameters. A simple and robust adaptive controller is designed such that the response system can be synchronized with a drive system with unknown parameters by utilizing Lyapunov stability theory and parameter identification. Our synchronization criteria are easily verified and do not need to solve any linear matrix inequality. This research also demonstrates the effectiveness of application in secure communication. Numerical simulations are carried out to illustrate the main results.

Adaptive Exponential Synchronization for Stochastic Competitive Neural Networks with Time-Varying Leakage Delays and Reaction-Diffusion Terms

“…Synchronization of competitive neural networks with different timescales has attracted a great interest [2][3][4][5][6][7]. In [7], Gan et al studied the adaptive synchronization for a class of competitive neural networks with different timescales and stochastic perturbation by constructing a Lyapunov-Krasovskii functional:…”

Parameter identification based on finite-time synchronization for Cohen–Grossberg neural networks with time-varying delays

“…In this paper, we introduce the adaptive controlling method to guarantee the finite-time synchronization and topology identification of CGNNs with uncertain parameters and time-varying delays. In this section, there are many results concerning the asymptotic or exponential synchronization and topology identification [11,13,14,19,22,23,31,34]. However, to the best of our knowledge, there is no results on the finite-time topology identification between two CGNNs with uncertain parameters and time-varying delays.…”

“…[9,10,20,29]. Up to now, a wide variety of approaches have been proposed for synchronization of chaotic systems, such as adaptive control [18,28], observer-based control [26], impulsive control [17,25], fuzzy control [16,37], coupling control [15], periodically intermittent control [4,12,13], and so on.…”

Adaptive Synchronization of Fractional Neural Networks with Unknown Parameters and Time Delays

“…Assumeâ ij = a ij ,b ij = b ij . The system (5) and (6) can be asymptotically synchronized under the following control strategy:…”

“…Meanwhile, synchronization of coupled neural networks has been investigated due to its potential applications in various engineering, including chaos generators design, secure communications, chemical and biological systems, information processing, distributed computation, optics, social science, harmonic oscillation generation, human heartbeat regulation and power system protection (see, e.g., [4][5][6][7][8][9][10][11] and the references therein). However, there are very limited results on the synchronization of fractional-order neural networks.…”