Exponential synchronization of chaotic neural networks with mixed delays and impulsive effects via output coupling with delay feedback

Synchronizing Neural Networks With Proportional Delays Based on a Class of -Type Allowable Time Scales

Without confines of the continuous-time domain, this paper addresses synchronization control problem of neural networks in the face of multiple proportional delays on general time scales. The idea to deal with proportional delays is to propose a class of -type allowable time scales on which we design an appropriate controller to achieve exponential synchronization based on a calculus theory on time scales and Lyapunov function/functional methods. It is shown that adopting properties of -type time scales is an effective approach to establish synchronization for the networks with proportional delays. This helps us to have insight into the synchronization problems on general intermittent time domain. Finally, simulation examples are given to illustrate the effectiveness of the theoretical results.

Lag Synchronization of Switched Neural Networks via Neural Activation Function and Applications in Image Encryption

This paper investigates the problem of global exponential lag synchronization of a class of switched neural networks with time-varying delays via neural activation function and applications in image encryption. The controller is dependent on the output of the system in the case of packed circuits, since it is hard to measure the inner state of the circuits. Thus, it is critical to design the controller based on the neuron activation function. Comparing the results, in this paper, with the existing ones shows that we improve and generalize the results derived in the previous literature. Several examples are also given to illustrate the effectiveness and potential applications in image encryption.

Non-fragile robust synchronization for Markovian jumping chaotic neural networks of neutral-type with randomly occurring uncertainties and mode-dependent time-varying delays

This paper deals with the problem of robust synchronization for uncertain chaotic neutral-type Markovian jumping neural networks with randomly occurring uncertainties and randomly occurring control gain fluctuations. Then, a sufficient condition is proposed for the existence of non-fragile output controller in terms of linear matrix inequalities (LMIs). Uncertainty terms are separately taken into consideration. This network involves both mode dependent discrete and mode dependent distributed time-varying delays. Based on the Lyapunov-Krasovskii functional (LKF) with new triple integral terms, convex combination technique and free-weighting matrices method, delay-dependent sufficient conditions for the solvability of these problems are established in terms of LMIs. Furthermore, the problem of non-fragile robust synchronization is reduced to the optimization problem involving LMIs, and the detailed algorithm for solving the restricted LMIs is given. Numerical examples are provided to show the effectiveness of the proposed theoretical results.

Synchronization of memristor-based recurrent neural networks with two delay components based on second-order reciprocally convex approach

We extend the notion of Synchronization of memristor-based recurrent neural networks with two delay components based on second-order reciprocally convex approach. Some sufficient conditions are obtained to guarantee the synchronization of the memristor-based recurrent neural networks via delay-dependent output feedback controller in terms of linear matrix inequalities (LMIs). The activation functions are assumed to be of further common descriptions, which take a broad view and recover many of those existing methods. A Lyapunov-Krasovskii functional (LKF) with triple-integral terms is addressed in this paper to condense conservatism in the synchronization of systems with additive time-varying delays. Jensen's inequality is applied in partitioning the double integral terms in the derivation of LMIs and then a new kind of linear combination of positive functions weighted by the inverses of squared convex parameters has emerged. Meanwhile, this paper puts forward a well-organized method to manipulate such a combination by extending the lower bound lemma. The obtained conditions not only have less conservatism but also less decision variables than existing results. Finally, numerical results and its simulations are given to show the effectiveness of the proposed memristor-based synchronization control scheme.