New Results on Exponential Convergence for HRNNs with Continuously Distributed Delays in the Leakage Terms

Authors:
Yuehua Yu;Weidong Jiao
Affiliations:
College of Mathematics and Computer Science, Hunan University of Arts and Science, Changde, People's Republic of China 415000;College of Engineering, Zhejiang Normal University, Jinhua, People's Republic of China 321004
Venue:
Neural Processing Letters
Year:
2014

Citing 3
Cited 0

New convergence behavior of high-order hopfield neural networks with time-varying coefficients

Journal of Computational and Applied Mathematics
Leakage Delays in T---S Fuzzy Cellular Neural Networks

Neural Processing Letters
Multistability and multiperiodicity of high-order competitive neural networks with a general class of activation functions

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper concerns with exponential convergence for a class of high-order recurrent neural networks with continuously distributed delays in the leakage terms. Without assuming the boundedness on the activation functions, some sufficient conditions are derived to ensure that all solutions of the networks converge exponentially to the zero point by using Lyapunov functional method and differential inequality techniques, which correct some recent results of Chen and Yang (Neural Comput Appl. doi:10.1007/s00521-012-1172-2, 2012). Moreover, we propose a new approach to prove the exponential convergence of HRNNs with continuously distributed leakage delays.