Title :
Nonsymmetric PDF estimation by artificial neurons: application to statistical characterization of reinforced composites
Author_Institution :
Fac. of Eng., Perugia Univ., Terni, Italy
fDate :
7/1/2003 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 estimate the probability density function of incoming input. It provides a low-order smooth robust estimate of the input signal probability density function. The presented method has been developed with reference to statistical characterization of polypropylene composites reinforced with vegetal fibers, that the proposed numerical experiments pertain to.
Keywords :
function approximation; information theory; learning (artificial intelligence); mathematics computing; neural nets; physics computing; probability; artificial neurons; generalized adaptive activation function neuron structure; information-theoretic-based principle; learning; low-order smooth robust estimate; neural network; nonsymmetric PDF estimation; numerical experiments; polypropylene composites; probability density function estimation; reinforced composites; statistical characterization; Econometrics; Entropy; Industrial engineering; Multilayer perceptrons; Neurons; Particle measurements; Probability density function; Random processes; Robustness; Signal processing;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2003.813825
