DocumentCode
1577237
Title
Continual neural networks
Author
Galushkin, A.I.
Author_Institution
Sci. Neurocomput. Centre, Acad. of Sci., Moscow, Russia
fYear
1992
Firstpage
1056
Abstract
It is necessary to introduce many parameters describing the structure and input signal of a pattern recognition system during the construction of open-loop structures of multilayer neural networks in order to provide maximum probability of correct recognition in practice. The availability of a large number of parameters, i.e., hundreds and thousands, poses some difficulties for learning and for the technical implementation of such networks. A transition to an attributes continuum and a continuum of neurons in the layer is considered for some specific neural network structures
Keywords
feedforward neural nets; pattern recognition; input signal; learning; multilayer neural networks; neurons; open-loop structures; pattern recognition system; Artificial neural networks; Concrete; Image sampling; Multi-layer neural network; Neural networks; Neurons; Optical fiber networks; Particle beam optics; Pattern recognition; Signal generators;
fLanguage
English
Publisher
ieee
Conference_Titel
Neuroinformatics and Neurocomputers, 1992., RNNS/IEEE Symposium on
Conference_Location
Rostov-on-Don
Print_ISBN
0-7803-0809-3
Type
conf
DOI
10.1109/RNNS.1992.268523
Filename
268523
Link To Document