DocumentCode
353260
Title
Stiefel-Grassman flow (SGF) learning: further results
Author
Fiori, Simone
Author_Institution
Dept. of Ind. Eng., Perugia Univ., Italy
Volume
3
fYear
2000
fDate
2000
Firstpage
343
Abstract
The aim of this this paper is to present recent contributions to Stiefel-Grassman flow (SGF) learning algorithms, a new class of learning paradigms for neural layers which allow for orthonormal signal/data processing. SGF learning has been introduced by the present author in 1996 as a way of training linear neural layers dedicated to blind source separation. In the meantime, several contributions have appeared in the scientific literature concerning the same topic, thus the study of a general framework explaining the different results has become necessary. In previous papers we presented a learning theory which appeared general enough to encompass the existing approaches; in this paper the latest results found are reported and discussed and references are given to computer simulations performed in order to test the effectiveness of the algorithms
Keywords
learning (artificial intelligence); multilayer perceptrons; signal processing; SGF learning; Stiefel-Grassman flow learning; blind source separation; linear neural layers; Blind source separation; Equations; Industrial engineering; Jacobian matrices; Neurons; Signal processing; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location
Como
ISSN
1098-7576
Print_ISBN
0-7695-0619-4
Type
conf
DOI
10.1109/IJCNN.2000.861328
Filename
861328
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