Title : 
Stability and Dissipativity Analysis of Distributed Delay Cellular Neural Networks
         
        
            Author : 
Feng, Zhiguang ; Lam, James
         
        
            Author_Institution : 
Dept. of Mech. Eng., Univ. of Hong Kong, Hong Kong, South Korea
         
        
        
        
        
            fDate : 
6/1/2011 12:00:00 AM
         
        
        
        
            Abstract : 
In this brief, the problems of delay-dependent stability analysis and strict (Q,S,ℜ)-α-dissipativity analysis are investigated for cellular neural networks (CNNs) with distributed delay. First, by introducing an integral partitioning technique, two new forms of Lyapunov-Krasovskii functionals are constructed, and improved distributed delay-dependent stability conditions are established in terms of linear matrix inequalities. Based on this criterion, a new sufficient delay and α-dependent condition is given to guarantee that the CNNs with distributed delay are strictly (Q,S,ℜ)-α-dissipative. The results developed in this brief can tolerate larger allowable delay than existing ones in the literature, which is demonstrated by several examples.
         
        
            Keywords : 
cellular neural nets; delay systems; integral equations; linear matrix inequalities; stability; CNN; Lyapunov-Krasovskii functional; delay-dependent stability analysis; distributed delay cellular neural network; integral partitioning technique; linear matrix inequalities; strict (Q,S,ℜ)-α-dissipativity analysis; Artificial neural networks; Asymptotic stability; Delay; Delay effects; Stability criteria; Symmetric matrices; Cellular neural networks; dissipativity; distributed delay; integral partitioning; Algorithms; Computer Simulation; Models, Theoretical; Neural Networks (Computer); Pattern Recognition, Automated;
         
        
        
            Journal_Title : 
Neural Networks, IEEE Transactions on
         
        
        
        
        
            DOI : 
10.1109/TNN.2011.2128341