Nonsymmetric PDF estimation by artificial neurons: application to statistical characterization of reinforced composites

Authors:
S. Fiori
Affiliations:
Fac. of Eng., Perugia Univ., Terni, Italy
Venue:
IEEE Transactions on Neural Networks
Year:
2003

Citing 0
Cited 4

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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.