DocumentCode
3467450
Title
Singular perturbations and time scales in artificial neural networks
Author
Moore, Kevin L. ; Naidu, D. Subbaram
Author_Institution
Coll. of Eng., Idaho State Univ., Pocatello, ID, USA
fYear
1991
fDate
11-13 Dec 1991
Firstpage
2932
Abstract
The learning and computing processes in a recursive neural network of the Hopfield type are identified as slow and fast phenomena. The corresponding dynamical equations are cast to fit into the framework of the theory of singular perturbations and time scales. The issues of degeneration and asymptotic expansions arising in obtaining approximate solutions are addressed
Keywords
Hopfield neural nets; learning (artificial intelligence); Hopfield neural nets; asymptotic expansions; dynamical equations; recursive neural network; singular perturbations; time scales; Artificial neural networks; Automatic control; Computer networks; Control systems; Differential equations; Equations; Hopfield neural networks; Intelligent networks; Manufacturing automation; Neural networks; Neurons; Nonlinear equations;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1991., Proceedings of the 30th IEEE Conference on
Conference_Location
Brighton
Print_ISBN
0-7803-0450-0
Type
conf
DOI
10.1109/CDC.1991.261077
Filename
261077
Link To Document