Title :
New sufficient conditions for absolute stability of neural networks
Author :
Liang, Xue-Bin ; Wu, Li-De
Author_Institution :
Dept. of Comput. Sci., Fudan Univ., Shanghai, China
fDate :
5/1/1998 12:00:00 AM
Abstract :
The main result obtained in this paper is that for a neural network with interconnection matrix T, if -T is quasi-diagonally row-sum or column-sum dominant, then the network system is absolutely stable. The above two sufficient conditions for absolute stability are independent of the existing sufficient ones in the literature. Under either of the above two sufficient conditions for absolute stability, the vector field defined by the network system is also structurally stable
Keywords :
absolute stability; neural nets; absolute stability; interconnection matrix; neural network; structural stability; sufficient conditions; vector field; Circuits; Computer science; Differential equations; Eigenvalues and eigenfunctions; Neural networks; Neurons; Stability; Structural engineering; Sufficient conditions; Symmetric matrices;
Journal_Title :
Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on
