Title :
Anti-Periodic Solutions for Shunting Inhibitory Cellular Neural Networks with Continuously Distributed Delays
Author :
Kang, Huiyan ; Si, Ligeng
Author_Institution :
Sch. of Math. & Phys., Changzhou Univ., Changzhou, China
Abstract :
This paper is concerned with the existence and exponential stability of anti-periodic solutions for shunting inhibitory cellular neural networks (SICNNs) with continuously distributed delays arising from the description of the neur-ons´ state in delayed neural networks. Without assuming the global Lipschitz and bounded conditions of activation functions, new sufficient conditions ensuring the existence and exponential stability of anti-periodic solutions for SICNNs are established. Moreover an example is given to illustrate the feasibility of the conditions in our results.
Keywords :
asymptotic stability; delays; neural nets; anti-periodic solutions; continuously distributed delays; delayed neural networks; exponential stability; shunting inhibitory cellular neural networks; Cellular neural networks; Delay; Manganese; Mathematics; Physics; Stability analysis; Sufficient conditions;
Conference_Titel :
Information Engineering and Computer Science (ICIECS), 2010 2nd International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-7939-9
Electronic_ISBN :
2156-7379
DOI :
10.1109/ICIECS.2010.5678221