Title :
New Results for Globally Asymptotic Stability and Instability of Recurrent Neural Networks
Author :
Yutian, Zhang ; Qi, Luo
Author_Institution :
Nanjing Univ. of Inf. Sci. & Technol., Nanjing
Abstract :
This paper presents four new theorems of globally asymptotic stability and instability for a general class of continuous-time recurrent neural networks with variant delay. With weaker conditions and less restrictive activation function, the obtained stability results improve and extend existing ones. Discussion and examples are given to illustrate and compare the new results with the old ones.
Keywords :
asymptotic stability; continuous time systems; delays; recurrent neural nets; continuous-time recurrent neural networks; globally asymptotic instability; globally asymptotic stability; variant delay; Asymptotic stability; Educational institutions; Electronic mail; Equations; Information science; Mathematics; Neural networks; Physics; Recurrent neural networks; Symmetric matrices; Globally Asymptotic Stability; Instability; Recurrent Neural Networks;
Conference_Titel :
Control Conference, 2007. CCC 2007. Chinese
Conference_Location :
Hunan
Print_ISBN :
978-7-81124-055-9
Electronic_ISBN :
978-7-900719-22-5
DOI :
10.1109/CHICC.2006.4347239
