Abstract
A novel blind signal separation algorithm based on bacterial foraging optimization was proposed. Fourth-order cumulant of signal was used as objective function for separation and Givens transform method was used for reducing the number of variables in objective function. The bacterial foraging optimization algorithm was used for optimizing the objective function transformed and source signal could be separated from mixed signal. Simulation results show that the separation algorithm based on bacterial foraging optimization can achieve the blind separation successfully and the separation precision is high.
The research grants National Natural Science Foundation of China 60802049.
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References
Jutten, C., Herault, J.: Blind separation of sources, Part I: An adaptive algorithm based on neuromimetic. Signal Processing 24(1), 1–10 (1991)
Comon, P.: Independent component analysis, a new concept? Signal Processing 36(3), 287–314 (1994)
Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine 22(3), 52–67 (2002)
Liu, Y., Passino, K.M.: Biomimicry of social foraging bacteria for distributed optimization models, principles, and emergent behaviours. Journal of Optimization Theory and Applications 115(3), 603–628 (2002)
Mishra, S.: A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation. IEEE Trans. on Evolutionary Computation 9(1), 61–73 (2005)
Majhi, R., Panda, G., Majhi, B., et al.: Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques. Expert Systems with Applications 36(6), 10097–10104 (2009)
Chu, Y., Shao, Z.B., Mi, H., et al.: An application of bacteria foraging algorithm in image compression. Journal of Shenzhen University Science and Engineering 25(2), 153–157 (2008)
Hyvarinen, A., Karhunen, J., et al.: Independent Component Analysis. Wiley, New York (2001)
Yi, Y.Q., Lin, Y.P., Lin, M., et al.: Blind source separation based on genetic algorithm. Journal of Computer Research and Development 43(2), 244–252 (2006)
Qin, H.R., Xie, S.L.: Blind separation algorithm based on QR decomposition and penalty function. Computer Engineering 29(17), 55–57 (2003)
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© 2011 Springer-Verlag Berlin Heidelberg
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Chen, L., Zhang, L., Liu, T., Li, Q. (2011). Blind Signal Separation Algorithm Based on Bacterial Foraging Optimization. In: Zhang, J. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23223-7_46
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DOI: https://doi.org/10.1007/978-3-642-23223-7_46
Publisher Name: Springer, Berlin, Heidelberg
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