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

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1 Author(s)
Fiori, S. ; Fac. of Eng., Perugia Univ., Terni, Italy

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.

Published in:

Neural Networks, IEEE Transactions on  (Volume:14 ,  Issue: 4 )

Date of Publication:

July 2003

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