Search

Abstract: In this paper, the problem of exponential stability of uncertain cellular neural networks with discrete and distribute time-varying delays is considered. Based on the Lyapunov function method and convex optimization approach, a new delay-dependent stability criterion with an exponential convergence rate is derived in terms of LMI (linear matrix inequality). Three numerical examples are given to show the effectiveness of the proposed methods. [Copyright &y& Elsevier]

Abstract: For neural networks with multiple time-varying delays, the problems on asymptotical stability are studied in this paper. Based on the Lyapunov method and the linear matrix inequality technique, some asymptotical stability criteria with good properties are derived. Numerical examples show the effectiveness of theoretical results. [Copyright &y& Elsevier]

Abstract: In this paper, a new nonlinear neural network for solving the shortest path problem is presented. The our nonlinear neural networks are able to generate optimal solutions to the shortest path problem. The performance of the our neural networks are demonstrated by means of illustrative example. The our neural networks are shown to be capable of generating the shortest path and suitable for electronic implementation. [Copyright &y& Elsevier]

Abstract: In this paper we represent two new methods for the solution of canonical form linear programming problems. In order to solve this linear programming problem we must minimize energy function of the corresponding neural network. Here energy function is considered as a Liapunov function and we use treated Hopfield neural network. First new method finds optimal solution for primal problem, using neural network, while second new method composes primal and dual problem and therefore finds optimal solution for both problems. Numerical results compared with simplex solution, and find that the convergence of two new methods to the correct solution is too fast, even faster than Neguyen’s method. The new methods are fully stable. [Copyright &y& Elsevier]

Abstract: The aim of this paper is the definition of a new model of neural network, called generalized cellular nonlinear network, that covers architectures and dynamics of the well known and widely used classes of feedforward neural networks and cellular neural networks. We show how cellular neural networks and feedforward neural networks can be derived from the general model: moreover we prove a theorem of existence and uniqueness for the solution of the system that describes the generalized cellular nonlinear network dynamics. These results are obtained using the method of Lyapunov’s finite majorizing equations that also represents a new approach in studying the stability of cellular neural networks. [Copyright &y& Elsevier]

This paper is concerned with a delay differential system with piecewise constant nonlinearity. Such a differential system can be regarded as a model of two-neuron artificial neural network with delayed feedback. Some interesting results are obtained for the convergence and periodicity of solutions of the system. [Copyright &y& Elsevier]