Abstract
When the source signals are known to be independent, positive and well-grounded which means that they have a non-zero pdf in the region of zero, a few algorithms have been proposed to separate these positive sources. However, in many practical cases, the independent assumption is not always satisfied. In this paper, a new approach is proposed to separate a class of positive sources which are not required to be independent. These source signals can be separated very quickly by using genetic algorithm. The objective function of genetic algorithm is derived from uncorrelated and some special assumptions on such positive source signals. Simulations are employed to illustrate the good performance of our algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aggarwal, C.C., Orlin, J.B., Tai, R.P.: Optimized Crossover for the Independent Set Problem. Operat. Res. 45(2), 226–234 (1997)
Baker, J.E.: Adaptive Selection Methos for Genetic Algorithms. In: Proc. ICAGA, vol. 1, pp. 101–111 (1985)
Comon, P.: Independent Component Analysis-A New Concept? Signal Process. 36(3), 287–314 (1994)
Cickocki, A., Karhunen, J., Kasprzak, W., Vigario, R.: Neural Networks for Blind Separation with Unknown Number of Sources. Neurocomputing 24(1), 55–93 (1999)
Cichocki, A., Amar, S.-i.: Adaptive Blind Signal and Image Processing. John Wiley and Sons, Chichester (2002)
De Jong, K.A.: Analysis of the Behavior of a Class of Genetic Adaptive Systems. PhD dissertation, Univerisity of Michigan (1975)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analsysis. John Wiley and Sons, Chichester (2001)
Jutten, C., Herault, J.: Blind Separation of Sources Part I: And Adaptive Algorithm Based on Neuromimetic Architecture. Signal Process. 24, 1–10 (1991)
Naanaa, W., Nuzillard, J.M.: Blind Source Separation of Positive and Partially Correlated Data. Signal Process. 85, 1711–1722 (2005)
Oja, E., Plumbley, M.D.: Blind Separation of Positive Sources by Globally Convergent Gradient Search. Neural Computation 16(9), 1811–1825 (2004)
Plumbley, M.D.: Conditions for Nonnegative Independent Component Analysis. IEEE Signal Processing Letters 9(6), 177–180 (2002)
Plumbley, M.D., Oja, E.: A ‘Nonnegative PCA’ Algorithm for Independent Component Analysis. IEEE Trans. Neural Networks 15(1), 66–76 (2004)
Rudolph, G.: Convergence Analysis of Canonical Genetic Algorithms. IEEE Trans. Neural Networks 5, 96–101 (1994)
Suzuki, J.: A Markov Chain Analysis on Simple Genetic Algorithms. IEEE Trans. System, Man and Cybernetics 25, 655–659 (1995)
Ye, M.: Global Convergence Analysis of a Discrete Time Nonnegative ICA Algorithm. IEEE Trans. Neural Networks 17(1), 253–256 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Ye, M., Gao, Z., Li, X. (2007). Blind Separation of Positive Signals by Using Genetic Algorithm. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_91
Download citation
DOI: https://doi.org/10.1007/978-3-540-72395-0_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
Online ISBN: 978-3-540-72395-0
eBook Packages: Computer ScienceComputer Science (R0)