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Target Identity Recognition Method based on Trusted Information Fusion

Volume 15, Number 4, April 2019, pp. 1235-1246
DOI: 10.23940/ijpe.19.04.p19.12351246

Lu Wanga,b, Chenglin Wena,c,d, and Lan Wud

aDepartment of Electrical Automation, Shanghai Maritime University, Shanghai, 201306, China
bDepartment of Image and Network Investigation, Railway Police College, Zhengzhou, 450053, China
cInstitute of Systems Science and Control Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China
dCollege of Electrical Engineering, Henan University of Technology, Zhengzhou, 450001, China


(Submitted on December 12, 2018; Revised on January 10, 2019; Accepted on February 16, 2019)


Safe and reliable target identity recognition is the important foundation of information security. In the complex environment of multi-source target information, in view of the potential impact of many uncertain factors on target identity recognition and the performance requirements of information security in the process of recognition, a trusted target identity recognition method is proposed in this paper. The BP neural network based on momentum factor is used to study and build an ensemble classification model, and based on this model, the trusted target identity recognition model is constructed. According to the relevant information characterized by the model, it can improve the recognition reliability of the target to a certain extent, thus providing more security and credibility for the recognition of the identified target. Finally, the effectiveness and feasibility of the proposed algorithm is verified by simulation experiments under an uncertain set environment.

References: 14

    1. X. B. Zhou, Y. Xu, and L. Zhang, “Research on Open Identity Authentication Model for PKI,” Journal of National University of Defense Technology, Vol. 35, No. 1, pp. 169-174, January 2013
    2. S. Wang, C. W. Chang, and Y. F. Wei, “USB Key Authentication Scheme based on Cloud Computing,” Application Research of Computers, Vol. 31, No. 7, pp. 2130-2134, July 2014
    3. J. Tian, C. Morillo, M. H. Azarian, and M. Pecht, “Motor Bearing Fault Detection using Spectral Kurtosis-based Feature Extraction Coupled with K -Nearest Neighbor Distance Analysis,” IEEE Transactions on Industrial Electronics, Vol. 63, No. 3, pp. 1793-1803, March 2016
    4. D. D. Chen, Y. J. Tian, and X. H. Liu, “Structural Nonparallel Support Vector Machine for Pattern Recognition,” Pattern Recognition, Vol. 60, pp. 296-305, December 2016
    5. L. Breiman, “Random Forests,” Machine Learning, Vol. 45, No. 1, pp. 5-32, January 2001
    6. Y. H. Cheng, X. Qiao, X. S. Wang, and Q. Yu, “Random Forest Classifier for Zero-Shot Learning based on Relative Attribute,” IEEE Transactions on Neural Networks and Learning Systems, Vol. 29, No. 5, pp. 1662-1674, May 2018
    7. G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, et al., “Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups,” IEEE Signal Processing Magazine, Vol. 29, No. 6, pp. 82-97, November 2012
    8. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” Journal of Machine Learning Research, Vol. 15, No. 1, pp. 1929-1958, June 2014
    9. W. X. Zheng and Y. Chen, “Stability Analysis of Time-Delay Neural Networks Subject to Stochastic Perturbations,” IEEE Transactions on Cybernetics, Vol. 43, No. 6, pp. 2122-2134, December 2013
    10. Z. W. Yu, D. X. Wang, Z. X. Zhao, C. L. P. Chen, J. You, H. S. Wong, et al., “Hybrid Incremental Ensemble Learning for Noisy Real-World Data Classification,” IEEE Transactions on Cybernetics, Vol. 99, pp. 1-14, December 2017
    11. J. Wang, Y. Wen, Y. Gou, Z. Y. Ye, and H. Chen, “Fractional-Order Gradient Descent Learning of BP Neural Networks with Caputo Derivative,” Neural Networks, Vol. 89, pp. 19-30, May 2017
    12. K. Hornik, “Approximation Capabilities of Multilayer Feedforward Networks,” Neural Networks, Vol. 4, No. 2, pp. 251-257, January 1991
    13. G. Shafer, “A Mathematical Theory of Evidence,” Princeton University Press, 1976
    14. F. Shi, X. C. Wang, L. Yu, et al., “30 Cases Analysis of MATLAB Neural Network,” Beihang University Press, 2010


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