燕子掌微弱电信号的计量研究
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摘要
利用自制的屏蔽室和屏蔽箱以及自制的铂金传感器接触式微弱电信号测试仪,初次获得了燕子掌(Crassula portulacea)自适应微弱电信号特性.采用小波软阈值消噪法对测试电信号进行消噪,并进行了时间序列的高斯径向基函数(RBF)神经网络预测.结果表明,采用RBF人工神经网络对植物微弱电信号进行短期预测是可行的.实现预测是在温室和/或塑料大棚生产中建立植物自适应智能化自动控制系统的关键环节.
The character of original weak electrical signals in Crassula portulacea was tested by a touching testing using platinum sensors in a system of self-made double shields.Tested data of electrical signals were denoised by the wavelet soft threshold using Gaussian radial base function(RBF) as the time series at a delayed input window chosen at 50.An intelligent RBF forecasting system was set up to predict signal in the plant.Results show that it is feasible to predict the weak electrical signal in the plant.Predicting data can be used as a key tache for constructing an intelligent automatic control system based on adaptive characteristics of plants to achieve energy savings of agricultural production in greenhouses and/or plastic lookums.
The character of original weak electrical signals in Crassula portulacea was tested by a touching testing using platinum sensors in a system of self-made double shields.Tested data of electrical signals were denoised by the wavelet soft threshold using Gaussian radial base function(RBF) as the time series at a delayed input window chosen at 50.An intelligent RBF forecasting system was set up to predict signal in the plant.Results show that it is feasible to predict the weak electrical signal in the plant.Predicting data can be used as a key tache for constructing an intelligent automatic control system based on adaptive characteristics of plants to achieve energy savings of agricultural production in greenhouses and/or plastic lookums.
引文
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[22]赵子恺.试论植物微弱电信号研究[J].中国计量学院学报,2002,13(3):253-257.
[2]娄成后.高等植物生长发育中物质运输与信息传递(二)[J].生物学通报,1991(12):1-3.
[3]任海云,王学臣.高等植物体内电信号存在的普遍性及其生理效应[J].植物生理学报,1993,19(1):97-101.
[4]王兰州,陈杰,柴中林.植物种群适应对策模型与生化机制[M].北京:科学出版社,2004:43-45.
[5]王兰州,李海霞,林敏,等.统计分析方法在植物电波信号研究中的应用[J].吉首大学学报,2006,27(3):67-70.
[6]李海霞,王兰州,李峤.君子兰叶片电信号的初步研究[J].中国计量学院学报,2005,16(1):62-65.
[7]郭金耀,杨晓玲.高等植物中的电信号研究进展[J].中国农学通报,2005,21(10),188-191.
[8]王兰州,曹文侠,令利军,等.石蒜叶片微弱电波动信号的测定[J].西北师范大学学报,2000,36(2):62-66.
[9]李海山,曹文侠,王兰州,等.油松与白皮松微弱电波信号振荡的研究[J].西北师范大学学报,2000,36(4):77-81.
[10]WANG L Z,LI Q,LI D S,et al.Analysis of electricalsignal inOsmanthus fragrans[C]∥The 6th InternationalSymposium on Instrumentation and Control Technology.USA:Proc of SPIE,2006,6357:N-1-7.
[11]李海霞.植物微弱电波信号的研究[D].北京:中国计量科学研究院,2006.
[12]李峤,王兰州,李东升,等.三种菊科植物电信号的分析[J].中国计量学院学报,2006,17(4):333-336.
[13]王忠义,陈端生,黄岚.温室植物生理指标监测及应用研究[J].农业工程学报,2000,16(2):101-104.
[14]郭庆胜,苏丛柏,陈翠荣.合欢树生物电位预测地震的机理探讨[J].山西地震,1999(增刊):25-27.
[15]丁锦丽,丁桂英,李海霞,等.ZJ0967杀虫剂作用下的黄瓜幼苗电信号研究[J].浙江科技学院学报,2006,18(3):180-184.
[16]李峤.植物电信号分析与处理研究[D].北京:中国计量科学研究院,2007.
[17]WANG L Z,LI H X,LI Q.2006 Studies on the plant e-lectric wave signal by the wavelet analysis[C]∥Tan Ji-ubin,Wen Xianfang,4th International Symposium on In-strumentation Science and Technology.Harbin:Harbin In-stitute of Technology Press,2006:520-525.
[18]DONOHO D L.De-moise by soft-thresholding[J].IEEETrans on IT,1995(3):613-327.
[19]NISHIDA S.Automatic detection method of P300 waveformin single sweep records by using a neural network[J].MedEng Phys,1994,16:425.
[20]韦保林,罗晓曙,汪秉宏,等.EEG信号的径向基函数神经网络预测[J].中国生物医学工程学报,2003,22(6):488-492.
[21]PARK J,SANDBERG I W.Approximation and radial-ba-sis-function networks[J].Neural Computation,1993,5(2):305-316.
[22]赵子恺.试论植物微弱电信号研究[J].中国计量学院学报,2002,13(3):253-257.
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