@article{(Open Science Index):https://publications.waset.org/pdf/9997445, title = {Improved Exponential Stability Analysis for Delayed Recurrent Neural Networks}, author = {Miaomiao Yang and Shouming Zhong}, country = {}, institution = {}, abstract = {This paper studies the problem of exponential stability analysis for recurrent neural networks with time-varying delay.By establishing a suitable augmented LyapunovCKrasovskii function and a novel sufficient condition is obtained to guarantee the exponential stability of the considered system.In order to get a less conservative results of the condition,zero equalities and reciprocally convex approach are employed. The several exponential stability criterion proposed in this paper is simpler and effective. A numerical example is provided to demonstrate the feasibility and effectiveness of our results. }, journal = {International Journal of Mathematical and Computational Sciences}, volume = {8}, number = {1}, year = {2014}, pages = {153 - 159}, ee = {https://publications.waset.org/pdf/9997445}, url = {https://publications.waset.org/vol/85}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 85, 2014}, }