DocumentCode
1736388
Title
Nonsymmetric PDF approximation by artificial neurons: application to statistical characterization of reinforced composites
Author
Fiori, Simone ; Burrascano, Pietro
Author_Institution
DIE-UNIPG, Perugia Univ., Italy
Volume
3
fYear
2002
fDate
6/24/1905 12:00:00 AM
Abstract
We present a generalized adaptive activation function neuron structure which learns through an information-theoretic-based principle, which is able to blindly estimate the probability density function of incoming input. We illustrate the behavior of the learning theory by the help of numerical experiments performed on real-world data with particular emphasis to statistical characterization of polypropylene composites reinforced with vegetal fibers.
Keywords
fibre reinforced composites; neural nets; probability; random processes; statistical analysis; artificial neurons; fibre reinforced composites; generalized adaptive activation function neuron structure; incoming input; information-theoretic-based principle; nonsymmetric PDF approximation; probability density function; real-world data; statistical characterization; Chemical industry; Density functional theory; Entropy; Mathematical model; Neurons; Optical fiber theory; Particle measurements; Probability density function; Random processes; Signal processing;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 2002. ISCAS 2002. IEEE International Symposium on
Print_ISBN
0-7803-7448-7
Type
conf
DOI
10.1109/ISCAS.2002.1010172
Filename
1010172
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