Abstract. The Herault-Jutten (HJ) algorithm is a neuromimetic structure capable to perform blind source separation (BSS) of a linear mixture from an array of sensors without knowing the transmission characteristics of the channels, nor the inputs. The learning algorithm developed by Herault and Jutten is based on the generalized Hebb’s rule in such a way that each output signal will be proportional to only one source by cancelling the influence of the other source. In this article, we show how theoretic stability conditions can be used for parameter estimation to restore the primary sources via interval computations.
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.