Hybrid independent component analysis by adaptive LUT activation function neurons.
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.
Fiori
Simone
S
Neural Networks and Adaptive Systems Research Group, Department of Industrial Engineering-University of Perugia, Numana, An, Italy. sfr@unipg.it
D000222:Adaptation, Physiological; D000818:Animals; D006801:Humans; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*