DocumentCode
2973680
Title
Performance improvement of LMS algorithm using Hopfield model network
Author
Takahasi, Kiyoshi ; Mori, Shinsaku
Author_Institution
Dept. of Electr. Eng., Keio Univ., Yokohama, Japan
fYear
1990
fDate
2-5 Dec 1990
Firstpage
1356
Abstract
An algorithm that improves the adaptation rate of the least-mean-square algorithm and is based on the dynamics of the network in the Hopfield (neural network) model is discussed. The rate of adaptation of the algorithm is shown to be n times as fast as the system of the well-known LMS algorithm with the same control gain, n being the number of iterations for each data sample. The convergence is shown to depend on the gain constant, not on n . Simulations of the convergence behavior of the algorithm are presented
Keywords
least squares approximations; neural nets; Hopfield model network; LMS algorithm; adaptation rate; control gain; convergence; data sample; gain constant; iterations; least-mean-square algorithm; network dynamics; neural network model; simulation; Adaptation model; Adaptive systems; Control systems; Convergence; Eigenvalues and eigenfunctions; Least squares approximation; Least squares methods; Neurons; Resonance light scattering; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Global Telecommunications Conference, 1990, and Exhibition. 'Communications: Connecting the Future', GLOBECOM '90., IEEE
Conference_Location
San Diego, CA
Print_ISBN
0-87942-632-2
Type
conf
DOI
10.1109/GLOCOM.1990.116715
Filename
116715
Link To Document