DocumentCode
1817413
Title
Learnings and applications of feedforward nets
Author
Li, Leong Kwan
Author_Institution
Dept. of Maths., Univ. of Southern California, Los Angeles, CA, USA
Volume
1
fYear
1992
fDate
7-11 Jun 1992
Firstpage
670
Abstract
A three-layer network which approximates a desired function f by a piecewise constant function is constructed. Backpropagation and classic gradient learning are present. A learning method is presented which gives the optimal weights at each iteration. Applications to pattern recognition are given with discussions on using RBF (radical basis function) unit networks. In addition, it is proved that the error is bounded by a linear function of the grid size
Keywords
backpropagation; feedforward neural nets; learning (artificial intelligence); pattern recognition; backpropagation; classic gradient learning; feedforward nets; linear function; optimal weights; pattern recognition; piecewise constant function; radical basis function; three-layer network; Fourier series; Interpolation; Learning systems; Mathematical model; Mathematics; Neurons; Pattern recognition; Polynomials; Supervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location
Baltimore, MD
Print_ISBN
0-7803-0559-0
Type
conf
DOI
10.1109/IJCNN.1992.287110
Filename
287110
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