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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
Nonsymmetric PDF estimation by artificial neurons: application to statistical characterization of reinforced
1Fac. of Eng., Perugia Univ., Terni, Italy.
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.
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