Some global properties such as global attractivity and global exponential stability for delayed Hopfield neural networks model, under the weaker assumptions on nonlinear activation functions, are concerned. By constru...Some global properties such as global attractivity and global exponential stability for delayed Hopfield neural networks model, under the weaker assumptions on nonlinear activation functions, are concerned. By constructing suitable Liapunov function, some simpler criteria for global attractivity and global exponential stability for Hopfield continuous neural network,; with time delays are presented.展开更多
In this paper,the existence,uniqueness and global attractivity are discussed on almost periodic solution of SICNNs（shunting inhibitory cellular neural networks）with continuously distributed delays.By using the fixed...In this paper,the existence,uniqueness and global attractivity are discussed on almost periodic solution of SICNNs（shunting inhibitory cellular neural networks）with continuously distributed delays.By using the fixed point theorem,differential inequality technique and Lyapunov functional method,giving the new ranges of parameters,several sufficient conditions are obtained to ensure the existence,uniqueness and global attractivity of almost periodic solution.Compared with the previous studies,our methods are more effective for almost periodic solution analysis of SICNNs with continuously distributed delays.Some existing results have been improved and extended.In order to show the effectiveness of the obtained results,an example is given in this paper.展开更多
We establish some stability results for delayed Hopfield Neural Network Model with variable coefficients and variable delays, by using the Lyapunov function. These stability criteria are new.
A Hopfield neural networks with delay is studied in this paper. An easily verifiable sufficient condition that guarantee the global attractivity of the Hopfield neural networks is obtained. An example is given to ill...A Hopfield neural networks with delay is studied in this paper. An easily verifiable sufficient condition that guarantee the global attractivity of the Hopfield neural networks is obtained. An example is given to illustrate that the conditions of our results are feasible.展开更多
文摘Some global properties such as global attractivity and global exponential stability for delayed Hopfield neural networks model, under the weaker assumptions on nonlinear activation functions, are concerned. By constructing suitable Liapunov function, some simpler criteria for global attractivity and global exponential stability for Hopfield continuous neural network,; with time delays are presented.
文摘In this paper,the existence,uniqueness and global attractivity are discussed on almost periodic solution of SICNNs（shunting inhibitory cellular neural networks）with continuously distributed delays.By using the fixed point theorem,differential inequality technique and Lyapunov functional method,giving the new ranges of parameters,several sufficient conditions are obtained to ensure the existence,uniqueness and global attractivity of almost periodic solution.Compared with the previous studies,our methods are more effective for almost periodic solution analysis of SICNNs with continuously distributed delays.Some existing results have been improved and extended.In order to show the effectiveness of the obtained results,an example is given in this paper.
文摘We establish some stability results for delayed Hopfield Neural Network Model with variable coefficients and variable delays, by using the Lyapunov function. These stability criteria are new.
基金Foundation for University Key Teacher by the Ministry of Education andthe NNSF of China and also by the Foundation of professo
文摘A Hopfield neural networks with delay is studied in this paper. An easily verifiable sufficient condition that guarantee the global attractivity of the Hopfield neural networks is obtained. An example is given to illustrate that the conditions of our results are feasible.
基金This Work was Supported by Distinguished Expert Science Foundation of Naval Aeronautical Institute and the Younger Foundation of Yantai University (SX06Z9)
