Title :
Comments on "A generalized LMI-based approach to the global asymptotic stability of delayed cellular neural networks"
Author_Institution :
Dept. of Comput. Sci. & Eng., Shanghai Jiao Tong Univ., China
fDate :
5/1/2005 12:00:00 AM
Abstract :
In this letter, we point out that the linear matrix inequality (LMI)-based criterion obtained in the above paper (Singh, IEEE Trans. Neural Netw., vol. 15, no. 1, p. 223-5, 2004) for the global exponential stability of the delayed neural networks can be simplified to a simpler but equivalent form and, thus, show that it is not necessary to have such complex form of condition in the above paper. As a result, we also answer the question raised by the author of the above paper.
Keywords :
asymptotic stability; cellular neural nets; delays; linear matrix inequalities; delayed cellular neural network; exponential stability; global asymptotic stability; linear matrix inequality; Asymptotic stability; Cellular networks; Cellular neural networks; Computer science; Linear matrix inequalities; Mathematical analysis; Neural networks; Stability criteria; Symmetric matrices; Delayed cellular neural networks (DCNNs); global exponential stability; linear matrix inequality (LMI); Algorithms; Computer Simulation; Linear Models; Neural Networks (Computer); Numerical Analysis, Computer-Assisted; Time Factors;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2005.844094