| 基于D-S证据理论的智能温室环境控制决策融合方法 |
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| 引用本文： | 孙力帆,张雅媛,郑国强,冀保峰,何子述.基于D-S证据理论的智能温室环境控制决策融合方法[J].农业机械学报,2018,49(1):268-275. |
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| 作者姓名： | 孙力帆 张雅媛 郑国强 冀保峰 何子述 |
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| 作者单位： | 河南科技大学,河南科技大学,河南科技大学,河南科技大学,电子科技大学 |
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| 基金项目： | 国家自然科学基金项目（U1504619、U1404615、61671139）、教育部产学合作协同育人项目（201602011005）和河南省科技攻关计划项目（162102210073） |
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| 摘 要： | 在无线传感器网络下的智能温室环境控制系统中,农作物的生长通常受多种环境因子共同作用。根据温室环境控制系统的实际需求建立基于Dempster-Shafer(D-S)证据理论的决策框架,并提出了一种数据预处理和决策融合方法。首先,使用箱线图检测量测数据中的异常值,考虑到现有直接剔除异常数据处理方法的弊端,提出了一种异常数据自适应修正方法;然后,利用加权平均距离聚类处理更新后的数据;最后,根据所提出的基于加权相似度的基本概率分配方法结合D-S证据理论进行融合,为温室环境控制做出正确决策。实验结果表明,箱线图检测异常数据更为准确,其检测率比狄克逊准则高近19.2%,对于不确定性融合结果,本文提出的基于加权相似度的基本概率分配方法相比现有方法降低了1~2个数量级,不仅可以提高温室环境参数融合精度,加快收敛速度,同时还能有效地降低决策风险。
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| 关 键 词： | 温室环境控制 决策融合 D-S证据理论 异常值自适应修正 聚类分析 |
| 收稿时间： | 2017/8/26 0:00:00 |
Approach to Decision Fusion for Intelligent Greenhouse Environmental Control Based on D-S Evidence Theory |
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| SUN Lifan,ZHANG Yayuan,ZHENG Guoqiang,JI Baofeng and HE Zishu.Approach to Decision Fusion for Intelligent Greenhouse Environmental Control Based on D-S Evidence Theory[J].Transactions of the Chinese Society of Agricultural Machinery,2018,49(1):268-275. |
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| Authors: | SUN Lifan ZHANG Yayuan ZHENG Guoqiang JI Baofeng and HE Zishu |
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| Institution: | Henan University of Science and Technology,Henan University of Science and Technology,Henan University of Science and Technology,Henan University of Science and Technology and University of Electronic Science and Technology of China |
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| Abstract: | In the intelligent greenhouse environment control system using wireless sensor networks, the crops growing is usually affected by various factors from the environment. The greenhouse control system makes inappropriate decisions based on the received measurements of wireless sensor networks, which will lead to go against the growth of crops. In view of this, a novel decision making framework based on Dempster-Shafer (D-S) evidence theory was established to meet practical requirements of the greenhouse environment control system. Moreover, two approaches of the data preprocessing and the decision fusion were proposed, respectively. Firstly, the measuring outliers data were detected by using the box-plot, and then an effective approach was proposed to correct them adaptively, which overcame the disadvantage of directly removing the outlier data in existing approaches. The corrected measuring data were also clustered by using the weighted average distance. Finally, a novel basic probability assignment approach was proposed to make correct decisions for the control of greenhouse environment based on the D-S evidence theory. Experimental results demonstrated that the outliers detection rate of the box-plot was more accurate than that of the Dixon criterion (nearly 19.2%). Compared with existing approaches, the fusion performance of the uncertainty was reduced by 1~2 order-of-magnitude in the proposed basic probability assignment approach, which not only increased precision of environmental indicators for the greenhouse control, but also accelerated the convergence process and reduced the risk of decision-makings effectively. |
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| Keywords: | greenhouse environment control decision fusion D-S evidence theory adaptive outlier correction cluster analysis |
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