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基于免疫网络算法关联函数经典域优化 被引量：2

Classical Fileds Optimum of Independent Function Based on Artificial Immune Network Algorithm
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摘要 可拓控制理论中的关联函数往往是影响可拓控制器好坏的重要因素,但如何定义出正确且客观的节域和经典域,也就是研究可拓理论中一个相当重要的课题.经典域往往依靠统计和专家经验给出,带有很多的主观性和随意性,这样确定的经典域所确定关联函数在可拓控制过程中,控制的结果可能不准确.为了更加客观准确的找到特征状态的经典域,以移动式起重机为研究对象,通过力矩平衡原理,利用起吊物种的力矩与车体能提供的最大力矩的比值作为适应函数,利用人工免疫网络算法寻找起重机在不同起吊物重情况下的仰角、旋转角的可操作的经典域,建立关联函数来起重机系统的危险程度. The independent function is an important factor affecting the extension control,so the study of classical fields is a key topic.Usually,people determine the classical fields by statistics or experts,and the incorrect classical fields would lead to the error results.In this article,the moment-equilibrium of mobile crane system is analyzed.The fitness function is ratio between the lifted objective moment and the maximal moment of crane body,and the classical fields of the rotation angel and the elevation is searched by artificial immune network algorithm.
作者 向长城
机构地区 湖北民族学院理学院
出处 《湖北民族学院学报（自然科学版）》 CAS 2009年第4期370-375,共6页 Journal of Hubei Minzu University(Natural Science Edition)
基金 湖北省教育厅优秀中青年项目(Q200729004 B20092901 Q201029004) 湖北省教育厅教学研究项目(2008300) 湖北民族学院博士启动基金(MY2008B037) 湖北民族学院青年基金(MY2008Q008)
关键词 免疫网络算法 关联函数 经典域 危险因子 immune network algorithm independent function classical fields danger factor
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献8

  • 1陈珍源,翁庆昌.基于滑模控制的可拓控制器设计[J].中国工程科学,2001,3(9):48-51. 被引量：26
  • 2杨春燕,蔡文.基于可拓集的可拓分类知识获取研究[J].数学的实践与认识,2008,38(16):184-191. 被引量：17
  • 3向长城,黄席樾.可拓免疫算法在汽轮机故障诊断中的应用[J].四川大学学报（工程科学版）,2008,40(2):141-146. 被引量：7
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二级参考文献14

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共引文献47

同被引文献26

  • 1杨春燕,蔡文.可拓工程[M].北京:科学出版社,2010:1-182.
  • 2DASGUPTAA D, YUA S, NINO F. Recent advances in ar- tificial immune system: models and application[J]. Applied Soft Computing, 2011, 11(2) : 1574-1587.
  • 3CHEN Guangzhu, ZHANG Lei, BAO Jiusheng. An im- proved negative selection algorithm and its application in the fault diagnosis of vibrating screen by wireless sensor networks [J]. Journal of Computational and Theoretical Nanoscience, 2013, 10(10): 2418-2426.
  • 4GAO X Z, WANG X, ZENGER K. Motor fault diagnosis u- sing negative selection algorithm [J]. Journal of Computing and Application, 2014, 25( 1): 55-65.
  • 5GONZALEZ F, DASGUPTA D, GOMEZ J. The effect of bi- nary matching rules in negative selection [C]//'Proceedings of the Genetic and Evolutionary Computation Conference. Chicago, USA, 2003 : 195-206.
  • 6GONZALEZ F, DASGUPTA D, NINO L F. A randomized real-valued negative selection algorithm [C]//Proceedings of the 2nd International Conference on Artificial Immune Systems. Edinburgh, UK, 2003: 261-272.
  • 7GONZALEZ F, DASGUPTA D, KOZMA R. Combining negative selection and classification techniques for anomaly detection[C]//Proceedings of the 2002 Congress on Evo- lutionary Computation. Honolulu, USA, 2002: 705-710.
  • 8AYARA M, TIMMIS J, De LEMOS R, et al. Negative se- lection: how to generate detectors[C]//Proceedings of 1st International Conference on Artificial Immune Systems. Canterbury, UK, 2002, 1: 89-98.
  • 9LEE C W, LEE D H. Design life analysis of KTX running gear systems on various operating conditions[J]. Key Engineering Materials, 2015, 625: 674-677.
  • 10CHAO K H. The application of extension algorithms in induction motor mechanical fault diagnosis[J]. Key Engineering Materials, 2010, 426/427: 593-598.

引证文献2

二级引证文献5

湖北民族学院学报（自然科学版）
2009年 第4期

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