Letters: The almost periodic solution of Lotka-Volterra recurrent neural networks with delays

Authors:
Yiguang Liu;Bingbing Liu;Sai Ho Ling
Affiliations:
Vision and Image Processing Laboratory, School of Computer Science and Engineering, Sichuan University, Chengdu, 610064, PR China;Data Storage Institute, A STAR, Singapore 138632, Singapore;Centre for Health Technologies, Faculty of Engineering and Information Technology, University of Technology Sydney, NSW, Australia
Venue:
Neurocomputing
Year:
2011

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Abstract

By the fixed-point theorem subject to different polyhedrons and some inequalities (e.g., the inequality resulted from quadratic programming), we obtain three theorems for the Lotka-Volterra recurrent neural networks having almost periodic coefficients and delays. One of the three theorems can only ensure the existence of an almost periodic solution, whose existence and uniqueness the other two theorems are about. By using Lyapunov function, the sufficient condition guaranteeing the global stability of the solution is presented. Furthermore, two numerical examples are employed to illustrate the feasibility and validity of the obtained criteria. Compared with known results, the networks model is novel, and the results are extended and improved.