The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, a Hopfield-type neural network with distributed delays and impulses are investigated. Some sufficient conditions are obtained for the global exponential stability and existence of the periodic solution to the Hopfield-type neural networks with distributed delay and impulsive on time scales.
By using the Lyapunov functional and some inequality technique, some sufficient conditions are obtained for the global exponential stability and existence of the periodic solution to the BAM neural networks with distributed delays on time scales. In fact, the results can be easily applied in practice.
In this paper, a class of BAM neural networks with impulses is studied on time scales. Some sufficient conditions of global exponential stability of the equilibrium point with impulses are obtained. At last, we give an example to demonstrate the effectiveness of the obtained results.
In this paper, we investigate a Hopfield-type neural network with distributed delays. Some sufficient conditions about the global exponential stability and existence of the periodic solution of the Hopfield-type neural networks with distributed delay are obtained on time scales.
In this paper, a class of BAM type Cohen-Grossberg neural networks with distributed delays are investigated. Some sufficient conditions about exponential stability of the distributed delays dynamic systems are obtained on time scales. The results extend and improve some recent works for neural networks with no impulses.
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.