工业工程与管理
工業工程與管理
공업공정여관리
INDUSTRIAL ENGINEERING AND MANAGEMENT
2014年
3期
85-90
,共6页
先验知识%实验设计%复杂过程%支持向量机
先驗知識%實驗設計%複雜過程%支持嚮量機
선험지식%실험설계%복잡과정%지지향량궤
prior knowledge%design of experiments%complex process%support vector machine
对于输入输出之间作用关系复杂且输出质量特性存在多极值的复杂过程,生产实践中通常拥有部分先验知识。以往研究中忽略这些先验知识进行实验设计建模导致样本浪费。提出一种基于先验知识的分区域式实验设计与建模方法。首先根据先验知识对各因子取值区间进行划分;其次利用模糊评价方法对波动大小进行度量;然后利用均匀设计方法在由各维度因子分段组合形成的子区域上安排试验点;最后,利用全部样本信息建立复杂作用关系过程的支持向量机回归模型。算例研究表明,与传统均匀实验设计建模方法所建模型相比,所提方法所建模型的三个预测误差指标值平均降低了18.1%,说明所提方法建立的模型具有更好的预测性能。
對于輸入輸齣之間作用關繫複雜且輸齣質量特性存在多極值的複雜過程,生產實踐中通常擁有部分先驗知識。以往研究中忽略這些先驗知識進行實驗設計建模導緻樣本浪費。提齣一種基于先驗知識的分區域式實驗設計與建模方法。首先根據先驗知識對各因子取值區間進行劃分;其次利用模糊評價方法對波動大小進行度量;然後利用均勻設計方法在由各維度因子分段組閤形成的子區域上安排試驗點;最後,利用全部樣本信息建立複雜作用關繫過程的支持嚮量機迴歸模型。算例研究錶明,與傳統均勻實驗設計建模方法所建模型相比,所提方法所建模型的三箇預測誤差指標值平均降低瞭18.1%,說明所提方法建立的模型具有更好的預測性能。
대우수입수출지간작용관계복잡차수출질량특성존재다겁치적복잡과정,생산실천중통상옹유부분선험지식。이왕연구중홀략저사선험지식진행실험설계건모도치양본낭비。제출일충기우선험지식적분구역식실험설계여건모방법。수선근거선험지식대각인자취치구간진행화분;기차이용모호평개방법대파동대소진행도량;연후이용균균설계방법재유각유도인자분단조합형성적자구역상안배시험점;최후,이용전부양본신식건립복잡작용관계과정적지지향량궤회귀모형。산례연구표명,여전통균균실험설계건모방법소건모형상비,소제방법소건모형적삼개예측오차지표치평균강저료18.1%,설명소제방법건립적모형구유경호적예측성능。
Generally,in production practices there is some prior knowledge of the complex process featured with multi-extremums of output quality characteristics and complex relationship between input parameters and output characteristics.Ignoring this prior knowledge could cause a waste of the sample in the design of experiments and modeling of the past researches.A sub-regional design of experiments and modeling approach based on the prior knowledge is proposed in this paper.Firstly,the value range of each factor is divided according to prior knowledge of a process;Secondly,fuzzy evaluation method was applied to measure the volatility;Thirdly,a uniform design (UD)was used to arrange trial points for each sub-region formed of the combination of sections of each factor in different dimensions;Lastly,a SVM regression model of the complex process was constructed using the information obtained from the sample.Case study results illustrate that,compared with the model obtained by traditional UD and modeling method, selected three prediction index values of the model obtained by the proposed approach are reduced by an average of 18.1%.It indicates that the model constructed by the proposed approach has better predictive performance.