Title :
On the complete stability of nonsymmetric cellular neural networks
Author :
Takahashi, Norikazu ; Chua, Leon O.
Author_Institution :
Dept. of Inf., Kyushu Univ., Kusuga, Japan
fDate :
7/1/1998 12:00:00 AM
Abstract :
This paper gives a new sufficient condition for complete stability of a nonsymmetric cellular neural network (CNN). The convergence theorem of the Gauss-Seidel method, which is an iterative technique for solving a linear algebraic equation, plays an important role in our proof. It is also shown that the existence of a stable equilibrium point does not imply complete stability of a nonsymmetric CNN.
Keywords :
cellular neural nets; convergence of numerical methods; iterative methods; stability; Gauss-Seidel method; complete stability; convergence theorem; iterative technique; linear algebraic equation; nonsymmetric CNN; nonsymmetric cellular neural networks; stable equilibrium point; Cellular neural networks; Circuit stability; Cloning; Convergence; Detectors; Gaussian processes; Image processing; Iterative methods; Nonlinear equations; Sufficient conditions;
Journal_Title :
Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on
