[1] WU Y, WANG Y, JIANG Y, et al. Fault prediction method of DC/DC converter based on characteristic parameter degradation [J]. Chinese Journal of Scientific Instrument, 2013, 34(6): 1380-1387. (吴祎,王友仁,姜媛媛,等. 基于特征参数退化的DC/DC变换器故障预测[J].仪器仪表学报, 2013,34(6):1380-1387.) [2] TU W, ZHANG G. Application of SVM multi-class classification in transformer fault diagnosis [J]. Journal of Computer Applications, 2010, 30(1): 96-98. (徒伟,张广明. 基于支持向量机的多类分类在变压器故障诊断中的应用[J]. 计算机应用, 2010, 30(1): 96-98.) [3] DANG X, JIANG T. Degradation prediction based on correlation analysis and assembled neural network [J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(1): 42-46. (党香俊,姜同敏. 基于相关分析和组合神经网络的退化预测[J]. 北京航空航天大学学报, 2013, 39(1): 42-46.) [4] ZHANG L, WANG Z, ZHAN S. Short-time fault prediction of mechanical rotating parts on the basis of fuzzy-grey optimizing method [J]. Mechanical Systems and Signal Processing, 2007, 21:856-865. [5] NIE J, ZHANG W. Study of fault forecast of low-voltage switchgear assembly based on fuzzy theory [J]. Application Research of Computers, 2009, 26(1): 215-217. (聂俊岚,张伟.基于模糊理论的低压开关设备故障预测研究[J].计算机应用研究, 2009, 26(1): 215-217.) [6] DING S, QI B, TAN H. An overview on theory and algorithm of support vector machines [J]. Journal of University of Electronic Science and Technology of China, 2011, 40(1): 2-10. (丁世飞,齐丙娟,谭红艳. 支持向量机理论与算法研究综述[J]. 电子科技大学学报,2011,40(1):2-10.) [7] DENG N, TIAN Y. Theory of support vector machine [M]. Beijing: Science Press, 2009: 43-56. (邓乃扬,田英杰.支持向量机理论、算法与拓展[M].北京: 科学出版社,2009: 43-56.) [8] YANG H, ZHOU Y, LIU H. Chaos optimization SVR algorithm with application in prediction of regional logistics demand[C]// Proceedings of the 1st International Conference Advances in Swarm Intelligence, LNCS 6146. Berlin: Springer-Verlag, 2010: 58-64. [9] SHI X, LIU H, YU S. Short-time load prediction based on support vector machine optimized by catfish particle swarm optimization algorithm [J]. Computer Engineering and Applications, 2013, 49(11): 220-223.(石晓艳,刘淮霞,于水娟. 鲶鱼粒子群算法优化支持向量机的短期负荷预测[J]. 计算机工程与应用,2013, 49(11):220-223.) [10] GENG J, SUN L, CHEN S. Parameters optimization of combined kernel function for support vector machine [J]. Journal of Computer Applications, 2013,33(5): 1321-1323.(耿俊豹,孙林凯,陈是学. 支持向量机的混合核函数参数优选方法[J]. 计算机应用, 2013,33(5): 1321-1323.) [11] ZHANG Y, ZHANG X, TANG L. Energy consumption prediction in ironmaking process using hybrid algorithm of SVM and PSO[J]. Proceedings of the 9th international conference on Advances in Neural Networks, LNCS 7368. Berlin: Springer-Verlag, 2012: 601-610. [12] SUN X, LU H, WU J. Passenger traffic volume forecasting based on support vector machine model optimized by ant colony algorithm [J]. Journal of Hefei University of Technology: Natural Science, 2012, 35(1): 125-129.(孙煦,陆化普,吴娟. 基于蚁群优化支持向量机模型的公路客运量预测[J]. 合肥工业大学学报:自然科学版,2012,35(1):125-129.) [13] GUAN X, GUO Q, ZHANG Z, et al. Radar emitter signal recognition based on kernel function SVM [J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2011, 31(4):188-194.(关欣,郭强,张政超,等. 基于核函数支持向量机的雷达辐射源识别[J]. 弹箭与制导学报,2011,31(4):188-191.) [14] COLORNI A, DORIGO M, MANIEZZO V, et al. Distributed optimization by ant colonies[C]// Proceedings of the 1st European Conference on Artificial Life. Cambridge: MIT Press, 1991: 134-142. [15] MAHDAVI M, FESANGHARY M, XDAMANGIR M. An improved harmony search algorithm for solving optimization problems [J]. Applied Mathematics and Computation, 2007, 188(2):1567-1579. [16] LI X, LIU Y, LI G, et al. Assessment of satellite health state based on fuzzy variable weight theory[J]. Systems Engineering and Electronics, 2014,36(3):476-480. (李鑫,刘莹莹,李赣华,等. 基于模糊变权原理的卫星健康评估方法[J]. 系统工程与电子技术,2014,36(3):476-480.) |