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Recent Patents on Engineering


ISSN (Print): 1872-2121
ISSN (Online): 2212-4047

Research Article

A Novel Classifier Design Algorithm Based on Gray Relation Theory

Author(s): Hui Han, Jingchao Li*, Xiang Chen and Yulong Ying

Volume 13, Issue 4, 2019

Page: [442 - 447] Pages: 6

DOI: 10.2174/1872212112666180828125338

Price: $65


Background: With the technical development of counter-reconnaissance and antijamming, communication system becomes more and more complex, and therefore, the recognition of communication signal becomes a challenging task according to recent patents. In order to achieve successful recognition and classification of radiation source signal under variant SNR environment, the design and selection of classifier are one of the key points.

Methods: Gray relation theory can solve the learning problem with a small number of samples and its algorithm is simple and can solve the issue of generality versus accuracy, which is very suitable for dealing with fuzzy mathematical problems. However, the selection of distinguishing coefficient has a direct effect on the recognition results by gray relation classifier. For conventional gray relation classifier, the distinguishing coefficient is usually set as a fixed value of 0.5, and for different types of signals, its recognition rate varies. Aiming at this issue, an improved adaptive gray relation classifier algorithm is proposed in the paper.

Results: The simulation results show that the recognition rate can still reach more than 87% even at the SNR of 10dB.

Conclusion: The proposed methods can improve the anti-jamming capability of the classifier, which can be widely used in the fields of electronic reconnaissance, fault diagnosis and image processing.

Keywords: Signal's recognition, classifier design, gray relation theory, different SNR environment, counter-reconnaissance, anti-jamming.

Graphical Abstract
S. Hao, W. Wang, and Y. Yan, "Class-wise dictionary learning for hyperspectral image classification", Neurocomputing, vol. 220, pp. 121-129, 2017.
J. Li, ""A new robust signal recognition approach based on holder cloud features under varying snr environment"", In: KSII Trans. Internet Info. Syst., vol. 9. pp. 4934-4949. 2015
J. Guo, Z. Li, and J. Wolf, "“Reliability centered preventive maintenance optimization for aircraft indicators,” In", 2016 Annual Reliability and Maintain ability Symposium (RAMS) AZ, USA 2016.
W. Wei, C. Zhen, and Y. Yan, "Recurrent face aging In ", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016pp. 2378-2386
W. Wei, Y. Yan, and W. Stefan, "Category-specific dictionary learning for attribute specific feature selection", IEEE Trans. Image Process., vol. 5, pp. 1465-1478, 2016.
J. Li, "A novel recognition algorithm based on holder coefficient theory and interval gray relation classifier", KSII Transactions on Internet and Information Systems (TIIS), vol. 9, pp. 4573-4584, 2015.
C. Kang, and X.A. Zhang, "Fusion algorithm of multiple classifiers for recognition of ships radiated_noises based on many-person decision-makings theory In", Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on IEEE,, 2007pp. 71-74
W. Wei, Y. Yan, and Z. Luming, "Collaborative sparse coding for multiview action recognition", EEE MultiMedia, vol. 23, pp. 80-87, 2016.
M. Najim, S. Puthucheri, and V. Agarwala, "ANN-based two-layer absorber design using Fe–Al hybrid nano-composites for broad bandwidth microwave absorption", IEEE Trans. Magn., vol. 52, pp. 1-8, 2016.
W. Silva, C. Freitas, and S. Sant’Anna, "PolSAR region classifier based on stochastic distances and hypothesis tests In ", Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International. IEEE, 2012, , pp. 1473-1476.
J. Li, and Y. Ying, "Radar signal recognition algorithm based on entropy theory In ", IEEE 2014 2nd International Conference on Systems and Informatics(ICSAI),, 2014pp. 718-723
J. Li, and Y. Ying, "Individual radiation source identification based on fractal box dimension In ", IEEE 2014 2nd International Conference on Systems and Informatics (ICSAI), , 2014pp. 676-768123
H. Han, J. Wang, and Q. Wu, ""Optimal wideband spectrum sensing order based on decision-making tree in cognitive radio" In ", Wireless Communications and Signal Processing (WCSP), 2010 International Conference on. IEEE , 2010pp. 1-5
X. Huang, and L. Zhang, "An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery", IEEE Trans. Geosci. Remote Sens., vol. 51, pp. 257-272, 2017.
J. Li, and J. Guo, "A new feature extraction algorithm based on entropy cloud characteristics of communication signals", Math. Probl. Eng., 2015.
L. Chen, B. Tian, and W. Lin, "Analysis and prediction of the discharge characteristics of the lithium–ion battery based on the Grey system theory", IET Power Electron., vol. 8, pp. 2361-2369, 2015.
L. Tang, D. Guo, and J. Wu, "Program evaluation and its application to equipment based on super-efficiency DEA and gray relation projection method", J. Sys. Eng. Elec., vol. 25, pp. 1037-1042, 2005.
L. Fei, and Y. Deng, "Meausre divergence degree of basic probability assignment based on Deng relative entropy In ", Control and Decision Conference (CCDC) 2016 Chinese. IEEE, 2016,, pp. 3857-3859
T. Wuxiang T., Some Shorteomings of Grey Absolute Correlation Degree. Sys. Eng., vol. 5, pp. 009, 1994.
H. Lee, N. Hayashi, and Y. Mizuno, Slope-assisted brillouin optical correlation-domain reflectometry using polymer optical fibers with high propagation loss., J. Lightwave Tech, 2017.
J.A. Michaels, and D.B. Chester, "Adaptive correlation techniques for spread spectrum communication systems In ", Military Communications Conference, MILCOM 2016-2016 IEEE, 2016pp. 678-681
L. Yibing, L. Jingchao, and L. Yun, "Classifier design algorithms aimed at overlapping characteristics", Info. Tech. J., vol. 11, pp. 1091-1096, 2012.
W.S. Powell, Information communicating apparatus and method., US Patent 4320387 , 1982.

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