Search

In this paper, a class of coupled memristive neural networks of neutral type with mixed time-varying delays via randomly occurring control is studied in order to achieve anti-synchronization. The model of the coupled memristive neural networks of neutral type with mixed time-varying delays is less conservative than those of traditional memristive neural networks. Some criteria are obtained to guarantee the anti-synchronization between the drive system and the response system. Two kinds of randomly occurring memristor-based controllers are designed. The analysis in this paper employs the differential inclusions theory, linear matrix inequalities, and the Lyapunov functional method. In addition, the new proposed results here are very easy to verify and also extend the results of earlier publications. Numerical examples are given to show the effectiveness of our results. [ABSTRACT FROM AUTHOR]

In this paper, the exponential and finite-time synchronization and parameters identification for uncertain multi-links complex network are investigated by using the impulsive control method. It is known that impulsive control, as an effective and ideal control technique, can not only realize the synchronization goal but also reduce the control cost. The paper proposes an impulsive complex network model with uncertain parameter. Two different kinds of adaptive feedback controllers are further designed based on impulsive delay differential inequalities, and the purpose is to achieve the exponential synchronization, finite-time synchronization and obtain parameters identification simultaneously. Several novel and useful exponential and finite-time synchronization criteria are also derived based on global exponential stability theory and finite-time stability theory. Finally, two numerical simulations are provided to illustrate the effectiveness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

In this paper, a novel adaptive composite nonlinear feedback controller is designed to synchronize complex networks. The proposed adaptive composite nonlinear feedback controller consists of an adaptive linear feedback control law and an adaptive nonlinear feedback control law. The adaptive linear feedback control part is designed to achieve a quick response. However, too fast convergent speed may cause overshoot or oscillations. So in order to achieve precise control, we design the adaptive nonlinear feedback control law to reduce the overshoot or oscillations caused by the adaptive linear feedback part. By using the properties of Weiner process and adding the proposed control strategy to the nodes, stochastic synchronization of complex networks with quickly convergent speed and high control precision can be achieved. Numerical simulations demonstrate the superior performance of this novel adaptive composite nonlinear feedback controller as compared to the conventional adaptive linear feedback controller. [ABSTRACT FROM AUTHOR]

In this paper, a novel adaptive nonlinear controller is designed to achieve stochastic synchronization of complex networks. We find that this novel adaptive nonlinear controller is less conservative and may be more widely used than the traditional adaptive linear controller. By using the properties of Weiner process, the stochastic synchronization of complex networks with stochastic perturbation via the proposed novel adaptive nonlinear controller can be achieved. Experimental tests demonstrate the superior performance of this novel adaptive nonlinear controller as compared to a conventional adaptive linear controller. [ABSTRACT FROM AUTHOR]

We explore a process for identifying the topology of networks. We find that it is possible to estimate the accurate topological structure of synchronous networks by analyzing their transient processes. Some novel conditions are given to ensure the uncertain connection topology approach to the true value. Our examples further illustrate the feasibility of these proposed methods. [ABSTRACT FROM AUTHOR]

Chaotic ant swarm (CAS) is an optimization algorithm based on swarm intelligence theory, which has been applied to find the global optimum solution in continuous search space. However, it is not able to solve the combinational optimization problem directly, e.g., the traveling salesman problem (TSP). To tackle this problem, we propose a new method to solve the traveling salesman problem based on chaotic ant swarm, CAS-TSP for short. The CAS-TSP is developed by introducing a mapping from continuous space to discrete space, reverse operator and crossover operator into the CAS. Computer simulations demonstrate that the CAS-TSP is capable of generating optimal solution to instances of the TSPLIB in almost all test problems of sizes up to 150. Also a comparative computational study shows that this CAS-TSP algorithm is an efficient tool for solving TSP and this heuristic is competitive also with other heuristics. [ABSTRACT FROM AUTHOR]