中国烟草科学
中國煙草科學
중국연초과학
CHINESE TOBACCO SCIENCE
2013年
4期
67-71
,共5页
曾建新%宫会丽%石硕%杨宁
曾建新%宮會麗%石碩%楊寧
증건신%궁회려%석석%양저
卷烟%感官评估%智能技术%支持向量机%可信度分析
捲煙%感官評估%智能技術%支持嚮量機%可信度分析
권연%감관평고%지능기술%지지향량궤%가신도분석
cigarette%sensory evaluation%intelligent technology%support vector machine%credibility analysis
为了改善大多数已建模型在预测时出现盲目的、机械的预测错误情况,以不同产地烤烟和白肋烟数据作为实验样本,综合集成假设检验、凸壳构造与内点分析、序列随机性检验等理论和方法,在预测控制环节设计了具有拒绝识别和可信度分析特征的分类器预测控制算法。实验结果表明,分类器能有效地接受与训练数据相似的测试样本,并给出凸壳内点测试样本的预测值和可信度参考值,同时亦能准确拒绝识别与烤烟质量数据差异较大的白肋烟和特异香型烤烟样本。不同类型测试数据实验验证了该算法的可行性和有效性,尤其是对于以专家经验或领域知识为主的卷烟质量评价问题更加实用。
為瞭改善大多數已建模型在預測時齣現盲目的、機械的預測錯誤情況,以不同產地烤煙和白肋煙數據作為實驗樣本,綜閤集成假設檢驗、凸殼構造與內點分析、序列隨機性檢驗等理論和方法,在預測控製環節設計瞭具有拒絕識彆和可信度分析特徵的分類器預測控製算法。實驗結果錶明,分類器能有效地接受與訓練數據相似的測試樣本,併給齣凸殼內點測試樣本的預測值和可信度參攷值,同時亦能準確拒絕識彆與烤煙質量數據差異較大的白肋煙和特異香型烤煙樣本。不同類型測試數據實驗驗證瞭該算法的可行性和有效性,尤其是對于以專傢經驗或領域知識為主的捲煙質量評價問題更加實用。
위료개선대다수이건모형재예측시출현맹목적、궤계적예측착오정황,이불동산지고연화백륵연수거작위실험양본,종합집성가설검험、철각구조여내점분석、서렬수궤성검험등이론화방법,재예측공제배절설계료구유거절식별화가신도분석특정적분류기예측공제산법。실험결과표명,분류기능유효지접수여훈련수거상사적측시양본,병급출철각내점측시양본적예측치화가신도삼고치,동시역능준학거절식별여고연질량수거차이교대적백륵연화특이향형고연양본。불동류형측시수거실험험증료해산법적가행성화유효성,우기시대우이전가경험혹영역지식위주적권연질량평개문제경가실용。
In order to improve mechanical and blind prediction behavior of some built models, a classifier prediction control algorithm was designed with flue-cured tobacco and burley tobacco in different producing areas as experimental samples. It had the characteristic of rejecting recognition and credibility analysis through integrating several theories and methods including hypothesis testing, convex hull, interior point analysis and sequence random testing. The results demonstrated that classifier could effectively accept test sample set and give predictive values and reliability reference values of test data in convex hull. In the meanwhile, classifier could also accurately reject burley tobacco sample and special type flue-cured sample, which was different from flue-cured sample set. The feasibility and validity of classifier were verified through different type of testing data, especially the practicality of cigarette sensory evaluation was based on expert experience or domain knowledge.