An ICA and AIS Based Method for Electromagnetic Compatibility Analysis

Article Preview

Abstract:

This paper proposes a novel approach for analyzing and predicting electromagnetic compatibility (EMC). The proposed method mainly consists of two parts. First, we separate input mixed signals from an electromagnetic environment by using fastICA, based on which, subsequently, we train a immune evolutionary network classifier (IENC). The classifier then finally could be used to analysis and predict electromagnetic compatibility. Experimental results have demonstrated the validity of the proposed algorithm.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 846-847)

Pages:

547-550

Citation:

Online since:

November 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] C.R. Paul: Electromagnetic Compatibility, John Wiley & Sons Inc., Hoboken, New Jersey (2005).

Google Scholar

[2] A. Hyvärinen, E. Oja: Independent component analysis: algorithms and applications, Neural networks, Vol. 13, No. 4 (2000), pp.411-430.

DOI: 10.1016/s0893-6080(00)00026-5

Google Scholar

[3] L.N. Castro, J. Timmis: Artificial immune systems: a new computational intelligence approach, Springer-Verlag UK, (2002).

Google Scholar

[4] R. Mutihac, M. Marc, V. Hulle: Comparison of Principle Component Analysis and Independent Component Analysis for Blind Source Separation. Romanian Reports in Physics, Vol. 56, No. 1 (2004), pp.20-32.

Google Scholar

[5] D. Dasgupta , Z. Ji, F. Gonzalez: Artificial immune system (AIS) research in the last five years. Proc. in 2013 IEEE Evolutionary Computation, Vol. 1 (2003), pp.123-130.

DOI: 10.1109/cec.2003.1299565

Google Scholar

[6] D. Dasgupta, S. Yu, F. Nino: Recent advances in artificial immune systems: models and applications. Applied Soft Computing, Vol. 11, No. 2 (2011), pp.1574-1587.

DOI: 10.1016/j.asoc.2010.08.024

Google Scholar

[7] J.I. Timmis: Artificial immune systems: a novel data analysis technique inspired by the immune network theory. University of Wales, (2000).

Google Scholar

[8] A.B. Watkins, L.C. Boggess: A resource limited artificial immune classifier. Proc. in 2013 IEEE Evolutionary Computation, Vol. 1 (2002), pp.926-931.

DOI: 10.1109/cec.2002.1007049

Google Scholar

[9] L.N. Castro, F.J. Von Zuben: The clonal selection algorithm with engineering applications. Proc. in GECCO, (2000) pp.36-39.

Google Scholar

[10] L.H. Wang: The Optimization and Classification Algorithms Based on Artificial Immune System and Applications. University of Hunan, 2009. (in Chinese).

Google Scholar