Title :
Almost periodicity in a harvesting Lotka-Volterra recurrent neural networks with mixed delays and impulses
Author :
Yao Xiaojie ; Qin Fajin
Author_Institution :
Dept. of Math. & Comput. Sci., Liuzhou Teachers Coll., Liuzhou, China
Abstract :
By using the theory of exponential dichotomy and Banach fixed point theorem, this paper is concerned with the problem of the existence and uniqueness of positive almost periodic solution in a harvesting Lotka-Volterra recurrent neural networks with mixed delays and impulses. To a certain extent, our work in this paper extend and improve some result in recent years. Finally, an example is given to illustrate the feasibility and effectiveness of the main result.
Keywords :
Volterra equations; delays; neural nets; Banach fixed point theorem; exponential dichotomy; harvesting Lotka-Volterra recurrent neural networks; mixed delays; mixed impulses; Computer science; Delays; Educational institutions; Electronic mail; Mathematics; Recurrent neural networks; Sufficient conditions; Impulses; Lotka-Volterra Recurrent Neural Networks; Mixed Delays; Positive Almost Periodic Solution;
Conference_Titel :
Control Conference (CCC), 2014 33rd Chinese
Conference_Location :
Nanjing
DOI :
10.1109/ChiCC.2014.6895809