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·350· 智能系统学报 第15卷 ity-based and grid-based cluster centers determination clus- 研究D].哈尔滨:哈尔滨工程大学,2010. tering algorithm[J].Control and decision,2017,32(5): GUAN Fengxu.Research on finger vein recognition 913-919 based on manifold learning and extension classifier[D]. [6]李亚,刘丽平,李柏青,等.基于改进K-Means聚类和 Harbin:Harbin Engineering University,2010. BP神经网络的台区线损率计算方法).中国电机工程 [16]赵燕伟,苏楠,张峰,等.基于可拓实例推理的产品族配 学报,2016,36(17):4543-4551. 置设计方法[U.机械工程学报,2010,46(15):146-154. LI Ya,LIU Liping,LI Baiqing,et al.Calculation of line ZHAO Yanwei,SU Nan,ZHANG Feng,et al.Configura- loss rate in transformer district based on improved K- tion design method for product family based on extension Means clustering algorithm and BP neural network[J].Pro- case reasoning[J].Journal of mechanical engineering, ceedings of the CSEE,2016,36(17):4543-4551. 2010,46(15):146-154. [7]邢长征,谷浩.基于平均密度优化初始聚类中心的k- [17刀叶永伟,张帆,王运.基于可拓距的起重机产品配置方 means算法[.计算机工程与应用,2014,50(20): 法设计.中国制造业信息化,2012,41(23):24-27. 135-138 YE Yongwei,ZHANG Fan,WANG Yun.The crane XING Changzheng,GU Hao.K-means algorithm based on products configuration design based on extension dis- average density optimizing initial cluster centre[J].Com- tance[J].Manufacturing information engineering of puter engineering and applications,2014,50(20):135-138. China.2012,41(23):2427. [8]张天骐,杨强,宋玉龙,等.一种K-means改进算法的软 [18]NOUAOURIA N.BOUKADOUM M.Case retrieval with 扩频信号伪码序列盲估计).电子与信息学报,2018, combined adaptability and similarity criteria:application 40(1):226-234 to case retrieval nets[C]//Proceedings of the 18th Interna- ZHANG Tianqi,YANG Qiang,SONG Yulong,et al.Blind tional Conference on Case-Based Reasoning.Research estimation PN sequence in soft spread spectrum signal of and Development.Alessandria,Italy,2010:242-256. improved K-means algorithm[J].Journal of electronics [19]赵燕伟,任设东,陈尉刚,等.基于改进BP神经网络的 information technology,2018,40(1):226-234. 可拓分类器构建[J.计算机集成制造系统,2015 [9]李晓瑜,俞丽颖,雷航,等.一种K-means改进算法的并 21(10):2807-2815 行化实现与应用).电子科技大学学报,2017,46(1) ZHAO Yanwei,REN Shedong,CHEN Weigang,et al. 61-68. 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K-means algorithm based on average density optimizing initial cluster centre[J]. Com￾puter engineering and applications, 2014, 50(20): 135–138. [7] 张天骐, 杨强, 宋玉龙, 等. 一种 K-means 改进算法的软 扩频信号伪码序列盲估计 [J]. 电子与信息学报, 2018, 40(1): 226–234. ZHANG Tianqi, YANG Qiang, SONG Yulong, et al. Blind estimation PN sequence in soft spread spectrum signal of improved K-means algorithm[J]. Journal of electronics & information technology, 2018, 40(1): 226–234. [8] 李晓瑜, 俞丽颖, 雷航, 等. 一种 K-means 改进算法的并 行化实现与应用 [J]. 电子科技大学学报, 2017, 46(1): 61–68. LI Xiaoyu, YU Liying, LEI Hang, et al. The parallel imple￾mentation and application of an improved K-means al￾gorithm[J]. Journal of University of Electronic Science and Technology of China, 2017, 46(1): 61–68. [9] TZORTZIS G, LIKAS A. The MinMax k-means cluster￾ing algorithm[J]. Pattern recognition, 2014, 47(7): 2505–2516. [10] ABUALIGAH L M, KHADER A T, AI-BETAR M A. 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ZHAO Yanwei, SU Nan, ZHANG Feng, et al. Configura￾tion design method for product family based on extension case reasoning[J]. Journal of mechanical engineering, 2010, 46(15): 146–154. [16] 叶永伟, 张帆, 王运. 基于可拓距的起重机产品配置方 法设计 [J]. 中国制造业信息化, 2012, 41(23): 24–27. YE Yongwei, ZHANG Fan, WANG Yun. The crane products configuration design based on extension dis￾tance[J]. Manufacturing information engineering of China, 2012, 41(23): 24–27. [17] NOUAOURIA N, BOUKADOUM M. Case retrieval with combined adaptability and similarity criteria: application to case retrieval nets[C]//Proceedings of the 18th Interna￾tional Conference on Case-Based Reasoning. Research and Development. Alessandria, Italy, 2010: 242–256. [18] 赵燕伟, 任设东, 陈尉刚, 等. 基于改进 BP 神经网络的 可拓分类器构建 [J]. 计算机集成制造系统, 2015, 21(10): 2807–2815. ZHAO Yanwei, REN Shedong, CHEN Weigang, et al. Extension classifier construction based on improved BP neural network[J]. Computer integrated manufacturing systems, 2015, 21(10): 2807–2815. [19] 李敏. K-means 算法的改进及其在文本聚类中的应用研 究 [D]. 无锡: 江南大学, 2018. LI Min. The research and application of text clustering based on improved K-means algorithm[D]. Wuxi: Jiang￾nan University, 2018. [20] 杨明极, 马池, 王娅, 等. 一种改进 K-means 聚类的 FCMM 算法 [J]. 计算机应用研究, 2019, 36(7): 2007–2010. YANG Mingji, MA Chi, WANG Ya, et al. Algorithm named FCMM to improve K-means clustering algorithm[J]. Application research of computers, 2019, 36(7): 2007–2010. [21] 韩俊, 谈健, 黄河, 等. 基于改进 K-means 聚类算法的供 电块划分方法 [J]. 电力自动化设备, 2015, 35(6): 123–129. HAN Jun, TAN Jian, HUANG He, et al. Power-supply￾ing block partition based on improved K-means cluster￾ing algorithm[J]. Electric power automation equipment, 2015, 35(6): 123–129. [22] 李武, 赵娇燕, 严太山. 基于平均差异度优选初始聚类 中心的改进 K-均值聚类算法 [J]. 控制与决策, 2017, [23] ·350· 智 能 系 统 学 报 第 15 卷
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