Hybrid independent component analysis by adaptive LUT activation function neurons

Authors:
Simone Fiori
Affiliations:
Neural Networks and Adaptive Systems Research Group, Department of Industrial Engineering--University of Perugia, I-60026, Numana, An, Italy
Venue:
Neural Networks
Year:
2002

Citing 19
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Abstract

The aim of this paper is to present an efficient implementation of unsupervised adaptive-activation function neurons dedicated to one-dimensional probability density estimation, with application to independent component analysis. The proposed implementation is a computationally light improvement to adaptive pseudo-polynomial neurons, recently presented in Fiori, S. (2000a). Blind signal processing by the adaptive activation function neurons. Neural Networks, 13(6), 597-611, and is based upon the concept of 'look-up table' (LUT) neurons.