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
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;
Conference_Titel :
Circuits and Systems, 2002. ISCAS 2002. IEEE International Symposium on
Print_ISBN :
0-7803-7448-7
DOI :
10.1109/ISCAS.2002.1010172
