控制与决策
控製與決策
공제여결책
CONTROL AND DECISION
2013年
6期
950-952
,共3页
灰色预测%GM(1,2) 模型%粒子群算法%相关系数
灰色預測%GM(1,2) 模型%粒子群算法%相關繫數
회색예측%GM(1,2) 모형%입자군산법%상관계수
grey forecasting%GM(1,2) model%particle swarm optimization%correlation coefficient
针对 GM(1,2)建模难点和模型缺陷提出两种改进方法:一是运用相关匹配算法,在历史数据库中搜索与主序列具有强关联特性的数据序列,确定为模型参考序列;二是引入粒子群算法,以模型预测性能评价指标为目标函数对模型参数进行辨识,改善模型预测性能.算例结果表明了改进方法的适用性和有效性.
針對 GM(1,2)建模難點和模型缺陷提齣兩種改進方法:一是運用相關匹配算法,在歷史數據庫中搜索與主序列具有彊關聯特性的數據序列,確定為模型參攷序列;二是引入粒子群算法,以模型預測性能評價指標為目標函數對模型參數進行辨識,改善模型預測性能.算例結果錶明瞭改進方法的適用性和有效性.
침대 GM(1,2)건모난점화모형결함제출량충개진방법:일시운용상관필배산법,재역사수거고중수색여주서렬구유강관련특성적수거서렬,학정위모형삼고서렬;이시인입입자군산법,이모형예측성능평개지표위목표함수대모형삼수진행변식,개선모형예측성능.산례결과표명료개진방법적괄용성화유효성.
@@@@Two improved method are proposed for the difficulty and defects of the traditional GM(1,2) model. Firstly, the correlation matching algorithm is adopted to search the reference sequence which has the maximum grey relation grade in the history database. Secondly, the particle swarm optimization algorithm is used to identify the model parameters by taking the evaluation function of the forecasting performance as the objective function. The simulation results show the effectiveness and the applicability of the presented method.
