贵州师范大学学报:自然科学版
貴州師範大學學報:自然科學版
귀주사범대학학보:자연과학판
Journal of Guizhou Normal University(Natural Sciences)
2012年
2期
95-102
,共8页
赵建忠%徐廷学%郭宏超%刘勇
趙建忠%徐廷學%郭宏超%劉勇
조건충%서정학%곽굉초%류용
加权最小二乘支持向量机%粗糙集%熵权%自适应粒子群优化%备件%消耗预测
加權最小二乘支持嚮量機%粗糙集%熵權%自適應粒子群優化%備件%消耗預測
가권최소이승지지향량궤%조조집%적권%자괄응입자군우화%비건%소모예측
weighted least squares support vector machine%rough set%entropy weight%adaptive particle swarm optimization%spare parts%consumption forecasting
在系统分析武器装备备件预测方法研究现状和导弹备件消耗特点的基础上,提出把粗糙集、熵权法、自适应粒子群优化算法与加权最小二乘支持向量机的组合预测模型应用于导弹备件消耗预测的构想。首先阐述了粗糙集、信息熵、自适应粒子群优化算法和加权最小二乘支持机的基本原理,并改进了自适应粒子群优化算法的搜索方式和最小二乘支持向量机的加权方法;然后建立了基于粗糙集、熵权法和自适应粒子群优化加权最小二乘支持向量机的导弹备件消耗预测模型,并分析了其实现过程。实例结果表明,所建立的组合预测模型在进行导弹备件消耗预测时具有较高的精度和重要的实用价值。
在繫統分析武器裝備備件預測方法研究現狀和導彈備件消耗特點的基礎上,提齣把粗糙集、熵權法、自適應粒子群優化算法與加權最小二乘支持嚮量機的組閤預測模型應用于導彈備件消耗預測的構想。首先闡述瞭粗糙集、信息熵、自適應粒子群優化算法和加權最小二乘支持機的基本原理,併改進瞭自適應粒子群優化算法的搜索方式和最小二乘支持嚮量機的加權方法;然後建立瞭基于粗糙集、熵權法和自適應粒子群優化加權最小二乘支持嚮量機的導彈備件消耗預測模型,併分析瞭其實現過程。實例結果錶明,所建立的組閤預測模型在進行導彈備件消耗預測時具有較高的精度和重要的實用價值。
재계통분석무기장비비건예측방법연구현상화도탄비건소모특점적기출상,제출파조조집、적권법、자괄응입자군우화산법여가권최소이승지지향량궤적조합예측모형응용우도탄비건소모예측적구상。수선천술료조조집、신식적、자괄응입자군우화산법화가권최소이승지지궤적기본원리,병개진료자괄응입자군우화산법적수색방식화최소이승지지향량궤적가권방법;연후건립료기우조조집、적권법화자괄응입자군우화가권최소이승지지향량궤적도탄비건소모예측모형,병분석료기실현과정。실례결과표명,소건립적조합예측모형재진행도탄비건소모예측시구유교고적정도화중요적실용개치。
On the basis of analyzing systemically present research condition of forecast method toward weapon and equipment spare parts and consumption characteristic of Missile spare parts, the paper brought forward the thought of applying forecast model composed of rough set, entropy weight , and weighted least squares support vector machine with adaptive particle swarm optimization to consumption forecasting of Missile spare parts. Firstly, the paper presented basic theory, improved on search mode of adaptive particle swarm optimization and weighted method of least squares support vector machine; Secondly, the paper established consumption forecasting model of Missile spare parts based on rough set ,entropy weight and weighted least squares support vector machine with adaptive particle swarm optimization, and analyzed its realization process. Lastly, the example results proved the combinatorial forecasting model have better forecast precision and important applied value in the course of consumption forecasting of Missile spare parts.