A model of Hopfield neural networks with continuously distributed delays is considered. A new sufficient condition which guarantees global exponential stability of an equilibrium point is given based on Lyapunov funct...A model of Hopfield neural networks with continuously distributed delays is considered. A new sufficient condition which guarantees global exponential stability of an equilibrium point is given based on Lyapunov functional approach and inequality technique. Compared with the previous results, our result provides a wider range since it possesses many adjustable parameters.展开更多
Without assuming the boundedness, strict monotonicity and differentiability of the activation functions, the authors utilize the Lyapunov functional method to analyze the global convergence of some delayed models. For...Without assuming the boundedness, strict monotonicity and differentiability of the activation functions, the authors utilize the Lyapunov functional method to analyze the global convergence of some delayed models. For the Hopfield neural network with time delays, a new sufficient condition ensuring the existence, uniqueness and global exponential stability of the equilibrium point is derived. This criterion concerning the signs of entries in the connection matrix imposes constraints on the feedback matrix independently of the delay parameters. From a new viewpoint, the bidirectional associative memory neural network with time delays is investigated and a new global exponential stability result is given.展开更多
文摘A model of Hopfield neural networks with continuously distributed delays is considered. A new sufficient condition which guarantees global exponential stability of an equilibrium point is given based on Lyapunov functional approach and inequality technique. Compared with the previous results, our result provides a wider range since it possesses many adjustable parameters.
基金Project supported by the National Natural Science Foundation of China (No.69982003, No.60074005).
文摘Without assuming the boundedness, strict monotonicity and differentiability of the activation functions, the authors utilize the Lyapunov functional method to analyze the global convergence of some delayed models. For the Hopfield neural network with time delays, a new sufficient condition ensuring the existence, uniqueness and global exponential stability of the equilibrium point is derived. This criterion concerning the signs of entries in the connection matrix imposes constraints on the feedback matrix independently of the delay parameters. From a new viewpoint, the bidirectional associative memory neural network with time delays is investigated and a new global exponential stability result is given.
