An ensemble learning algorithm for blind signal separation problem
Paper
Paper/Presentation Title | An ensemble learning algorithm for blind signal separation problem |
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Presentation Type | Paper |
Authors | Li, Yan (Author) and Wen, Peng (Author) |
Journal or Proceedings Title | Proceedings of the 2005 International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA 2005) |
ERA Conference ID | 43263 |
Journal Citation | 1, pp. 1196-1200 |
Number of Pages | 5 |
Year | 2005 |
Place of Publication | Washington, DC, united States |
ISBN | 0769525040 |
Web Address (URL) of Paper | http://doi.ieeecomputersociety.org/10.1109/CIMCA.2005.1631425 |
Conference/Event | 2005 International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA 2005) |
International Conference on Computational Intelligence for Modelling, Control and Automation | |
Event Details | International Conference on Computational Intelligence for Modelling, Control and Automation CIMCA Rank C C C C C C C C C C C C C C C |
Event Details | 2005 International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA 2005) Event Date 28 to end of 30 Nov 2005 Event Location Vienna, Austria |
Abstract | The framework in Bayesian learning algorithms is based on the assumptions that the quantities of interest are governed by probability distributions, and that optimal decisions can be made by reasoning about these probabilities together with the data. In this paper, a Bayesian ensemble learning approach based on enhanced least square backpropagation (LSB) neural network training algorithm is proposed for blind signal separation problem. The method uses a three layer neural network with an enhanced LSB training algorithm to model the unknown blind mixing system. Ensemble learning is applied to estimate the parametric approximation of the posterior probability density function (pdf). The Kullback- Leibler information divergence is used as the cost function in the paper. The experimental results on both artificial data and real recordings demonstrate that the proposed algorithm can separate blind signals very well |
Keywords | backpropagation; belief networks; blind source separation; least squares approximations; neural nets; statistical distributions; Bayesian ensemble learning; Kullback-Leibler information divergence; LSB neural network training algorithm; blind signal separation problem; enhanced least square backpropagation; parametric approximation; posterior probability density function; probability distribution |
ANZSRC Field of Research 2020 | 400607. Signal processing |
461399. Theory of computation not elsewhere classified | |
469999. Other information and computing sciences not elsewhere classified | |
Public Notes | © 2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Byline Affiliations | Department of Mathematics and Computing |
Department of Mechanical and Mechatronic Engineering |
https://research.usq.edu.au/item/9zy53/an-ensemble-learning-algorithm-for-blind-signal-separation-problem
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