| 摘 要: 针对智能温室控制模型中，实时温度数据因通信和设备故障等问题造成的数据缺失现象，提出了在常规神经网络模型基础上，利用模糊控制进行补偿的插补方法。利用搭建在温室大棚的智能监控系统对人工温室中的温度参数进行采集，并利用所测数据对上述模型进行插补验证实验。实验结果表明：与通用模型相比，所提出的模糊神经网络插补模型改变了传统处理方式中插补数据不精确的现状，为实时温度数据缺失提供了有效地处理方法，也为建立智能温室模型提供了数据基础。 |
| 关键词: 缺失数据；智能温室；数据插补；模糊神经网络 |
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中图分类号: TP399
文献标识码: A
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| Research on Data Interpolation Technology of Intelligent Greenhouse Control Model under Missing Data |
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CHEN Xiuyu
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( Higher Vocational Technical College, Dalian Neusoft University of Information, Dalian 116023, China)
chenxiuyu@neusoft.edu.cn
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| Abstract: Aiming at real-time temperature data missing caused by communication and equipment failure in intelligent greenhouse control model, this paper proposes an interpolation method based on conventional neural network model and fuzzy control for compensation. An intelligent monitoring system built in the greenhouse is used to collect temperature parameters in the artificial greenhouse, and the measured data is used to perform an interpolation verification experiment on the above model. The experimental results show that: compared with the general model, the proposed fuzzy neural network interpolation model improves the precision of interpolation data in traditional processing methods, provides an effective processing method for real-time temperature data missing, and lays a data foundation for the establishment of intelligent greenhouse model. |
| Keywords: missing data; intelligent greenhouse; data interpolation; fuzzy neural network |
