Title :
Sufficient condition for convergence of a relaxation algorithm in actual single-layer neural networks
Author :
Zurada, Jacek M. ; Shen, Weigong
Author_Institution :
Dept. of Electr. Eng., Louisville Univ., KY, USA
fDate :
12/1/1990 12:00:00 AM
Abstract :
Application of the contraction mapping theorem to single-layer feedback neural networks of a gradient-type is discussed. The sufficient condition for stability of a relaxation algorithm in actual continuous-time networks is derived and illustrated with an example. Results showing the stability of a numerical solution obtained with the relaxation algorithm are presented
Keywords :
convergence of numerical methods; neural nets; relaxation theory; stability; continuous-time networks; contraction mapping theorem; convergence; relaxation algorithm; single-layer neural networks; stability; sufficient condition; Character recognition; Convergence; Equations; Gaussian noise; Intelligent networks; Multilayer perceptrons; Nearest neighbor searches; Neural networks; Sonar detection; Sufficient conditions;
Journal_Title :
Neural Networks, IEEE Transactions on
