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Journal of Applied Mathematics
Volume 2013, Article ID 935491, 8 pages
Research Article

Global Robust Attractive and Invariant Sets of Fuzzy Neural Networks with Delays and Impulses

1Center of Engineering Mathematics and Department of Applied Mathematics, Kunming University of Science and Technology, Kunming, Yunnan 650093, China
2Department of Mathematics, Yuxi Normal University, Yuxi, Yunnan 653100, China

Received 15 October 2012; Accepted 21 January 2013

Academic Editor: Huijun Gao

Copyright © 2013 Kaihong Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A class of fuzzy neural networks (FNNs) with time-varying delays and impulses is investigated. With removing some restrictions on the amplification functions, a new differential inequality is established, which improves previouse criteria. Applying this differential inequality, a series of new and useful criteria are obtained to ensure the existence of global robust attracting and invariant sets for FNNs with time-varying delays and impulses. Our main results allow much broader application for fuzzy and impulsive neural networks with or without delays. An example is given to illustrate the effectiveness of our results.