Hybrid independent component analysis by adaptive LUT activation function neurons

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.