Blind Source Separation for Changing Source Number: A Neural Network Approach with a Variable Structure
Shun-Tian Lou, Xian‐Da Zhang · 2001
Blind source separation (BSS) problems have recently become an active research area in both statistical signal processing and unsupervised neural learning. In most approaches, the number of source signals is typically assumed to be known a priori, but this does not usually hold in practical applications. Although the problem of determining the unknown source number has been studied recently, the BSS problem when the source number is changing dynamically is not yet considered. The main objective of this paper is to study and solve these two problems. Its basic idea is to utilize the correlation coe cients between output components of the neural network (NN) as a mean for determining the unknown source number and/or detecting dynamical change of the source number, and is to develop a neural network with variable structure to perform the corresponding adaptive blind source separation.