Meyer-Base, Anke and Gruber, Peter and Theis, Fabian and Foo, Simon (2006) Blind source separation based on self-organizing neural network. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 19 (3). pp. 305-311. ISSN 0952-1976, 1873-6769
Full text not available from this repository. (Request a copy)Abstract
This contribution describes a neural network that self-organizes to recover the underlying original sources from typical sensor signals. No particular information is required about the statistical properties of the sources and the coefficients of the linear transformation, except the fact that the source signals are statistically independent and nonstationary. This is often true for real life applications. We propose an online learning solution using a neural network and use the nonstationarity of the sources to achieve the separation. The learning rule for the network's parameters is derived from the steepest descent minimization of a time-dependent cost function that takes the minimum only when the network outputs are uncorrelated with each other. In this process divide the problem into two learning problems one of which is solved by an anti-Hebbian learning and the other by an Hebbian learning process. We also compare the performance of our algorithm with other solutions to this task. (C) 2005 Elsevier Ltd. All rights reserved.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | INDEPENDENT COMPONENT ANALYSIS; SIGNALS; self-organization; independent component analysis; Hebbian and anti-Hebbian learning |
| Subjects: | 500 Science > 570 Life sciences |
| Divisions: | Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang > Arbeitsgruppe Dr. Fabian Theis |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 17 Feb 2021 07:46 |
| Last Modified: | 17 Feb 2021 07:46 |
| URI: | https://pred.uni-regensburg.de/id/eprint/34732 |
Actions (login required)
 |
View Item |
