DocumentCode
299249
Title
Convergence of Hopfield neural network for orthogonal transformation
Author
Kamio, Takeshi ; Ninomiya, Hiroshi ; Asai, Hideki
Author_Institution
Fac. of Eng., Shizuoka Univ., Hamamatsu, Japan
Volume
1
fYear
1995
fDate
30 Apr-3 May 1995
Firstpage
493
Abstract
In this paper, we describe the convergence of the discrete Walsh transform (DWT) processor based on Hopfield neural networks. First, the influence of the orthonormal matrix on solving linear equations by the steepest descent (SD) method is investigated and this theory is applied to the convergence of Hopfield neural networks. Finally, it is shown both analytically and by simulation that this type of network is suitable for orthogonal transforms
Keywords
Hopfield neural nets; Walsh functions; convergence of numerical methods; equations; matrix algebra; transforms; Hopfield neural network; convergence; discrete Walsh transform processor; linear equations; orthogonal transformation; orthonormal matrix; simulation; steepest descent method; Application software; Convergence; Discrete transforms; Discrete wavelet transforms; Equations; Hopfield neural networks; Neural networks; Signal processing; Symmetric matrices; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 1995. ISCAS '95., 1995 IEEE International Symposium on
Conference_Location
Seattle, WA
Print_ISBN
0-7803-2570-2
Type
conf
DOI
10.1109/ISCAS.1995.521558
Filename
521558
Link To Document