DocumentCode
1242478
Title
Design of a perceptron-like algorithm based on system identification techniques
Author
Saeren, M.
Author_Institution
IRIDIA Lab., Univ. Libre de Bruxelles
Volume
6
Issue
2
fYear
1995
fDate
3/1/1995 12:00:00 AM
Firstpage
504
Lastpage
506
Abstract
We develop a new adjustment rule for a perceptron with a saturating nonlinearity that ensures perfect classification when the input patterns are linearly separable. The proof is based on the Lyapunov stability formalism, is widely used in deterministic process identification, and is rather straightforward. It should therefore be of pedagogical interest
Keywords
Lyapunov methods; pattern classification; perceptrons; stability; Lyapunov stability formalism; adjustment rule; deterministic process identification; pattern classification; perceptron-like algorithm; saturating nonlinearity; system identification techniques; Adaptive control; Algorithm design and analysis; Associative memory; Circuit synthesis; Dispersion; Hopfield neural networks; Network synthesis; Neural networks; Scattering; System identification;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.363489
Filename
363489
Link To Document