The Comprehensive Diagnostic Method Combining Rough Sets and Evidence Theory

[1] Dempester A. P. Upper and Lower Probabilities Induced by a Multi-valued Mapping, Annals Mathematical Statistics, no. 38, pp. 325–339, 1967. DempesterA. P. Upper and Lower Probabilities Induced by a Multi-valued Mapping Annals Mathematical Statistics 38 325 339 1967 10.1214/aoms/1177698950 Search in Google Scholar

[2] Shafer G. A Mathematical Theory of Evidence. Princeton: Princeton University Press, pp. 133–185, 1976. ShaferG. A Mathematical Theory of Evidence Princeton Princeton University Press 133 185 1976 10.1515/9780691214696 Search in Google Scholar

[3] Laurie Webster, Jen-Gwo Chen, Simon S. et al. Validation of Authentic Reasoning Expert Systems, Information Sciences, no. 117, pp. 19–46, 1999. WebsterLaurie ChenJen-Gwo SimonS. Validation of Authentic Reasoning Expert Systems Information Sciences 117 19 46 1999 10.1016/S0020-0255(99)00005-5 Search in Google Scholar

[4] Zhu Lijun, Hu Zheng, Yang Yongmin. Fault Diagnosis Based on Reasoning Integration of Rough Sets and Evidence Theory, Transactions of CSICE, vol. 25, no.1, pp. 90–95, 2007. ZhuLijun HuZheng YangYongmin Fault Diagnosis Based on Reasoning Integration of Rough Sets and Evidence Theory Transactions of CSICE 25 1 90 95 2007 Search in Google Scholar

[5] Li Yanhong, Guo Haixia. Fault Diagnosis of Mine Belt Conveyor Based on Improved DS Evidence Theory, Coal Mine Machinery, vol. 41, no. 8, pp. 174–176, 2020. LiYanhong GuoHaixia Fault Diagnosis of Mine Belt Conveyor Based on Improved DS Evidence Theory Coal Mine Machinery 41 8 174 176 2020 Search in Google Scholar

[6] Jia Jin Zhang, Hen Yi Nuo, Ke Ding Lin. Risk analysis of a Bayesian network for harmful chemicals road transportation systems based on fuzzy sets and improved Dempster/Shafer (DS) evidence theory, Journal of Beijing University of Chemical Technology (Natural Science), vol. 47, no. 1, pp. 38–45, 2020. ZhangJia Jin NuoHen Yi LinKe Ding Risk analysis of a Bayesian network for harmful chemicals road transportation systems based on fuzzy sets and improved Dempster/Shafer (DS) evidence theory Journal of Beijing University of Chemical Technology (Natural Science) 47 1 38 45 2020 Search in Google Scholar

[7] Pawlak Z. Rough Sets, International Journal of Computer and Information Sciences, vol. 11, no. 5, pp. 341–356, 1982. PawlakZ Rough Sets International Journal of Computer and Information Sciences 11 5 341 356 1982 10.1007/BF01001956 Search in Google Scholar

[8] Yunliang J, Congfu X, Jin G, et al. Research on Rough Set Theory Extension and Rough Reasoning, IEEE International Conference on Systems, Man and Cybernetics, Hague, pp. 5888–5893, 2004. YunliangJ CongfuX JinG Research on Rough Set Theory Extension and Rough Reasoning IEEE International Conference on Systems, Man and Cybernetics Hague 5888 5893 2004 Search in Google Scholar

[9] Yao Y Y, Lingras P J. Interpretations of Belief Functions in the Theory of Rough Sets, Information Sciences, no. 104, pp. 81–106, 1998. YaoY Y LingrasP J Interpretations of Belief Functions in the Theory of Rough Sets Information Sciences 104 81 106 1998 10.1016/S0020-0255(97)00076-5 Search in Google Scholar

[10] Skowron A, Grzymalta-Busse J. From Rough Set Theory to Evidence Theory-Advances in the Dempster-Shafer Theory of Evidence. New York: John Wiley & Sons, Inc, pp. 193–236, 1994. SkowronA Grzymalta-BusseJ From Rough Set Theory to Evidence Theory-Advances in the Dempster-Shafer Theory of Evidence New York John Wiley & Sons, Inc 193 236 1994 Search in Google Scholar

[11] Ding Han, Hou Ruichun, Ding Xiangqian. A Fault Diagnosis Method Based on Rough Set and Improved D-S Evidence Theory, Computer & Digital Engineering, vol. 47, no. 3, pp. 543–549, 2019. DingHan HouRuichun DingXiangqian A Fault Diagnosis Method Based on Rough Set and Improved D-S Evidence Theory Computer & Digital Engineering 47 3 543 549 2019 Search in Google Scholar

[12] Zhang Wenxiu. The Theory and Method of Rough Sets, Beijing: Science Press, 2001. ZhangWenxiu The Theory and Method of Rough Sets Beijing Science Press 2001 Search in Google Scholar

[13] Sun Quan, Ye Xiuqing, Gu Weikang. A New Combination Rule of Evidence Theory, ACTA Electronic SINICA, vol. 28, no. 8, pp. 117–119, 2000. SunQuan YeXiuqing GuWeikang A New Combination Rule of Evidence Theory ACTA Electronic SINICA 28 8 117 119 2000 Search in Google Scholar

[14] Yager R R. On the D-S Framework and New Combination Rules, Information Sciences, vol. 41, no. 2, pp. 93–138, 1987. YagerR R On the D-S Framework and New Combination Rules Information Sciences 41 2 93 138 1987 10.1016/0020-0255(87)90007-7 Search in Google Scholar

[15] Li Bicheng, Wang Bo, Wei Jun. An Efficient Combination Rule of Evidence Theory, Journal of Data Aquisition & Processing, vol. 17, no. 1, pp. 33–36, 2002. LiBicheng WangBo WeiJun An Efficient Combination Rule of Evidence Theory Journal of Data Aquisition & Processing 17 1 33 36 2002 Search in Google Scholar

[16] Zhao Rongyong, Zhang Hao, Li Cuiling. The Study and Application of Discretization Model for Continuous Attribute Values in Rough Set Theory, Computer Engineering and applications, vol. 41, no.8, pp. 40–42, 91, 2005. ZhaoRongyong ZhangHao LiCuiling The Study and Application of Discretization Model for Continuous Attribute Values in Rough Set Theory Computer Engineering and applications 41 8 40 42 91 2005 Search in Google Scholar

[17] Xu Dong, Wang Xin, Meng Yulong, etc. A Discretization Algorithm Based on Forest Optimization Network and Variable Precision Rough Set, Journal of Northwestern Polytechnical University, vol. 38, no. 2, pp. 434–441, 2020. XuDong WangXin MengYulong A Discretization Algorithm Based on Forest Optimization Network and Variable Precision Rough Set Journal of Northwestern Polytechnical University 38 2 434 441 2020 10.1051/jnwpu/20203820434 Search in Google Scholar

[18] Yang Guang, Wu Xiaoping, Song Yexin, etc. Muli-sensor Information Fusion Fault Diagnosis Method Based on Rough Set Theory, Systems Engineering and Electronics, vol. 31, no.8, pp. 2013–2019, 2009. YangGuang WuXiaoping SongYexin Muli-sensor Information Fusion Fault Diagnosis Method Based on Rough Set Theory Systems Engineering and Electronics 31 8 2013 2019 2009 Search in Google Scholar

[19] Yang Guang, Yu Shuofeng. Synthesized fault diagnosis method reasoned from rough set-neural network and evidence theory, Concurrency Computat Pract Exper. 2018; e4944. https://doi.org/10.1002/cpe.4944. YangGuang YuShuofeng Synthesized fault diagnosis method reasoned from rough set-neural network and evidence theory Concurrency Computat Pract Exper 2018 e4944 https://doi.org/10.1002/cpe.4944. 10.1002/cpe.4944 Search in Google Scholar

[20] He You, Wang Guohong, Lu Dajin, etc. Multi-sensor Information Fusion with Applications, Beijing: Electronic Industry Press, 2000. HeYou WangGuohong LuDajin Multi-sensor Information Fusion with Applications Beijing Electronic Industry Press 2000 Search in Google Scholar

[21] Vesanto J, Alhoniemi E. Clustering of the Self-organizing Map, IEEE-Neural Networks, no. 11, pp. 586–598, 2000. VesantoJ AlhoniemiE Clustering of the Self-organizing Map IEEE-Neural Networks 11 586 598 2000 10.1109/72.84673118249787 Search in Google Scholar