| 一种新的温度传感器湿度补偿方法 |
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| 引用本文： | 邹水平,行鸿彦,于祥. 一种新的温度传感器湿度补偿方法[J]. 南京气象学院学报, 2016, 8(6): 533-539 |
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| 作者姓名： | 邹水平 行鸿彦 于祥 |
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| 作者单位： | 南京信息工程大学 气象灾害预报预警与评估协同创新中心, 南京, 210044;南京信息工程大学 江苏省气象探测与信息处理重点实验室, 南京, 210044;南京信息工程大学 气象灾害预报预警与评估协同创新中心, 南京, 210044;南京信息工程大学 江苏省气象探测与信息处理重点实验室, 南京, 210044;南京信息工程大学 气象灾害预报预警与评估协同创新中心, 南京, 210044;南京信息工程大学 江苏省气象探测与信息处理重点实验室, 南京, 210044 |
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| 基金项目： | 国家自然科学基金（61671248）；江苏省产学研联合创新资金计划（BY2013007-02）；江苏省高校自然科学研究重大项目（15KJA460008）；江苏省“信息与通信工程”优势学科建设工程项目；江苏省“六大人才高峰”计划 |
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| 摘 要： | 针对温度传感器测量中易受湿度影响的问题，通过对思维进化算法（Mind Evolutionary Algorithm，MEA）中的趋同操作、异化操作及收敛条件进行研究改进，对趋同操作中的散布权值进行自适应调整，在异化操作中引入差分进化算法的变异操作，并考虑收敛条件中搜索平面平缓的情况，提出了基于改进思维进化算法的BP神经网络湿度补偿方法.由湿度影响检定实验得到的样本数据，利用此补偿方法建立湿度补偿模型，将补偿结果与未经优化的BP神经网络模型的结果进行比较研究.结果表明，基于改进思维进化算法的BP神经网络模型补偿精度较高，收敛速度快，计算量小，可有效提高温度传感器的测量精度和可靠性，便于实际应用.
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| 关 键 词： | 温度传感器 思维进化算法 BP神经网络 湿度补偿 |
| 收稿时间： | 2015-12-21 |
A new method of humidity compensation for temperature sensor |
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| ZOU Shuiping,XING Hongyan and YU Xiang. A new method of humidity compensation for temperature sensor[J]. Journal of Nanjing Institute of Meteorology, 2016, 8(6): 533-539 |
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| Authors: | ZOU Shuiping XING Hongyan YU Xiang |
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| Affiliation: | Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044;Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044;Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044;Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044;Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044;Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044 |
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| Abstract: | In order to solve the problem that the temperature sensor is susceptible to humidity during measurement,this paper takes some improvement researches into advolution operation,alienation operation and convergence conditions in mind evolutionary algorithm,and makes some adaptive adjustment of the distributing weights in the convergence operation.Differential operation is introduced into alienation operation and the situation with flat searching plane is considered.A humidity compensation method using improved mind evolutionary algorithm of BP neural network model is established,according to the sample data obtained from the experiment of humidity influence.Compared with general BP neural network model,the improved mind evolutionary algorithm of BP neural network model has higher compensation precision,faster convergence and less computation load,which can improve the accuracy and reliability of the temperature sensor effectively and is convenient for applications. |
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| Keywords: | temperature sensor Mind Evolutionary Algorithm BP neural network humidity compensation |
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