Abstract
AbstractOf concern is a Cohen–Grossberg neural network (CGNNs) system taking into account distributed and discrete delays. The class of delay kernels ensuring exponential stability existing in the previous papers is enlarged to an extended class of functions guaranteeing more general types of stability. The exponential and polynomial (or power type) type stabilities becomes particular cases of our result. This is achieved using appropriate Lyapunov-type functionals and the characteristics of the considered class.
Publisher
Springer Science and Business Media LLC
Reference24 articles.
1. Bouzerdoum, A.; Pattison, T.R.: Neural network for quadratic optimization with bound constraints. IEEE Trans. Neural Netw. 4(3), 293–304 (1993)
2. Cohen, M.; Grossberg, S.: Absolute stability and global pattern formation and parallel memory storage by competitive neural networks. IEEE Trans. Syst. Man Cybern. 13(5), 815–826 (1983)
3. Cui, B.T.; Wu, W.: Global exponential stability of Cohen-Grossberg neural networks with distributed delays. Neurocomputing. 72(1–3), 386–391 (2008)
4. Faria, T.; Oliveira, J.J.: General criteria for asymptotic and exponential stabilities of neural network models with unbounded delays. Appl. Math. Comput. 217(23), 9646–9658 (2011)
5. Faria, T.; Gadotti, M.C.; Oliveira, J.J.: Stability results for impulsive functional differential equations with infinite delay. Nonlinear Anal. Theory Methods Appl. 75(18), 6570–6587 (2012)
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献