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基于现代战争非线性特征的军事训练思考 被引量:3

Reflections on the Military Training Based on the Nonlinear Characteristics in Modern Warfare
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摘要 信息化条件下的战斗力体系已经成为一个复杂的大系统,该作战系统在战斗力生成、战场布势、作战方式等方面呈现出明显的非线性特征。军事训练必须适应现代战争特点的非线性要求,在作战要素多样化、大纵深训练以及系统非线性危险的预防等方面应进一步加强。 The battle effectiveness system under the information circumstances has become a compli- cated great system, and it has presented obvious nonlinear characteristics in battle effectiveness building, battlefield disposition and battle manners, etc. The military training must adapt for the nonlinear charac- teristics request of modern warfare, and it should try to improve on the battle element' s diversification, large depth training and the preventive of system nonlinear risk.
机构地区 解放军理工大学
出处 《系统科学学报》 CSSCI 2012年第2期89-92,共4页 Chinese Journal of Systems Science
关键词 现代战争 非线性特征 军事训练 Modern warfare Nonlinear characteristics Military training
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  • 1王泽晖.运用非线性思维认识现代战争[J].国防科技,2008,29(6):12-14. 被引量:2
  • 2向科元等.重视战斗力要素间的非线性关系.[EB/OL].[2009-07-22 ]. http: // mil. news. sina. com. en/2009-07-22/2323212097. html.
  • 3季本林.联合作战应先在课堂打响[N].解放军报,2010.9.28:10.

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  • 1王保华,李雄.美国陆军数字化部队的IC^4ISR系统[J].现代军事,1999,0(10):36-38. 被引量:1
  • 2傅惠民.导弹命中精度整体推断方法[J].北京航空航天大学学报,2006,32(10):1141-1145. 被引量:9
  • 3Bednar E M. Feasibility Study of Variance Reduction in the Thun- der Campaign - level Model[ D ]. Alabama :Air University,2005.
  • 4Stone G F, Mclntyre G A. The Joint Warfare System (JWARS) :a Modeling and Analysis Tool for the Defense Department [ C ]. Pro- ceedings of the 2001 Winter Simulation Conference,2001.
  • 5Vapnik V N. An Overview of Statistical Learning Theory[ J ]. IEEE Trans on Neural Networks, 1999,10 ( 5 ) :988-999.
  • 6Yuan S F, Chu F L. Support Vector Machines-based Fault Diagnosis for Turbo-Pump Rotor[J]. Mechanical Systems and Signal Process- ing,2006,20(4) :939-952.
  • 7张军,胡晓敏,罗旭耀,等.蚁群优化[M].清华大学出版社,2007.
  • 8Shelokar P S, Jayaraman V K, Kulkarni B D. An ant colony approach for clustering[J]. Analytica Chimica Acta, 2004, 509 (2): 187-195.
  • 9Zhao J, Wang X, Wu Z. Forecasting GDP growth based on Ant Colony Clustering Algorithm and RBF neural network[C]// Automation and Logistics, 2008.1CAL 2008. IEEE International Conference on. IEEE, 2008:1839-1843.
  • 10江敬灼.论作战实验方法[J].军事运筹与系统工程,2009,23(3):8-15. 被引量:15

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