Global Exponential Robust Stability of High-Order Hopfield Neural Networks with S-Type Distributed Time Delays-Reference-Cited by-同舟云学术

Global Exponential Robust Stability of High-Order Hopfield Neural Networks with S-Type Distributed Time Delays

Published:2014 Issue: Volume:2014 Page:1-8
ISSN:1110-757X
Container-title:Journal of Applied Mathematics
language:en
Short-container-title:Journal of Applied Mathematics

Author:

Zheng Haiyong¹^ORCID,Wu Bin¹^ORCID,Wei Tengda²,Wang Linshan²,Wang Yangfan³

Affiliation:

1. College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China

2. School of Mathematical Sciences, Ocean University of China, Qingdao 266100, China

3. College of Marine Life Science and School of Mathematical Sciences, Ocean University of China, Qingdao 266100, China

Abstract

By employing differential inequality technique and Lyapunov functional method, some criteria of global exponential robust stability for the high-order neural networks with S-type distributed time delays are established, which are easy to be verified with a wider adaptive scope.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Applied Mathematics

Link

http://downloads.hindawi.com/journals/jam/2014/705496.pdf

Reference37 articles.

1. Exponential Adaptive Lag Synchronization of Memristive Neural Networks via Fuzzy Method and Applications in Pseudorandom Number Generators

2. Global Synchronization in an Array of Delayed Neural Networks With Hybrid Coupling

3. Stability and Hopf Bifurcation in a Simplified BAM Neural Network With Two Time Delays

4. Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays

5. Associative Learning of Integrate-and-Fire Neurons with Memristor-Based Synapses

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