基于神经网络的光纤荧光海藻测量理论及应用研究
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摘要
海洋中的浮游植物是海洋生态系统中能量的主要转换者和最主要的生产者,海藻则是海洋中主要的浮游植物,检测海洋中海藻的种类和浓度,可以估测海域生态系统的群落结构和能量分布状态,实现对海洋污染的监测和治理。
本文根据荧光测量机理和人工神经网络理论,提出了将荧光法与BP神经网络相结合的单一海藻种类识别和浓度测量方法。通过分析荧光测量的基本原理、特点、海藻荧光检测机理,研究不同种类、不同浓度海藻溶液及其所含特征色素的吸收光谱、激发光谱、荧光光谱特性,提取海藻种类识别和浓度测量的特征参量,为设计光纤荧光测量系统提供了理论和实验依据。
提出光纤荧光测量系统的总体方案,设计测量系统的光路部分,其中包括:光源的选择及驱动电路的设计,滤光及光的传输的实现,光耦合方法及结构设计等;设计微弱荧光信号检测方案,其中包括:光电二极管的选择及电路设计,光电倍增管的选择及电路设计,微弱信号检测技方法的实现。
在对荧光法检测海藻和人工神经网络理论进行分析的基础上,应用BP网络进行三种不同门类海藻的识别,并对相应种类海藻进行浓度预测。设计海藻种类识别BP网络及海藻浓度预测BP网络。
对三种不同门类单一海藻的种类识别和浓度预测实验结果进行分析,验证神经网络应用于光纤荧光海藻检测的方法的可行性。
本研究将荧光法和人工神经网络模式识别和参数预测理论相结合应用于光纤海藻测量系统,能对三种不同门类的海藻进行种类识别和浓度测量,具有数据实时采集、处理及结果显示等功能,为海藻状态监测提供了一种新手段。
Phytoplankton in the sea is the main energy transducer and producer in ocean ecological system, and algae are the major marine phytoplankton. By recogning algae and measuring its concentration, we can estimate community structure and energy distribution in the sea area and realize monitoring and governing ocean pollution.
This paper proposes a combination of fluorescence method and BP neural networks to realize single algal species identification and concentration measurement. By analyzing the basic principles and characteristics of fluorescence measurement and algae fluorescence detection mechanism, studying the different types and different concentrations solution of seaweed and characteristics of pigments contained in the absorption spectra, excitation spectra, fluorescence spectral characteristics,extract characteristic parameter for algae species identification and concentration measurement ,provide theoretical and experimental basis for the design of optical fiber fluorescence measurement system.
Propose total designation scheme of the optical fiber fluorescence measurement system,design optical part of the measurement system, including: the choice of light source and drive circuit design, the realization of filter and optical transmission, optical coupling method and structural design, etc.;design weak fluorescent signal detection method, including: the choice of photodiode and circuit design, the choice of photomultiplier tubes and circuit design, the realization of weak signal detection.
Based on analysing the theory of fluorescence detection and artificial neural network, apply BP network to recognize three kinds of algae, and then measure its concentration,design algal species identification BP network and algae concentration predicted BP network
Analysis the categories identification and concentration estimation results of three different kinds of algae, verify the feasibility of this method.
This research applies fluorescence method and artificial neural network pattern recognition and Parameter estimation theory to optical fiber algae monitor system that can realize recognition and concentration estimation of three kind of algae. This system has real time data collection, data processing and results displaying functions. A novel technical means for algae state monitoring is provides by the paper.
本文根据荧光测量机理和人工神经网络理论,提出了将荧光法与BP神经网络相结合的单一海藻种类识别和浓度测量方法。通过分析荧光测量的基本原理、特点、海藻荧光检测机理,研究不同种类、不同浓度海藻溶液及其所含特征色素的吸收光谱、激发光谱、荧光光谱特性,提取海藻种类识别和浓度测量的特征参量,为设计光纤荧光测量系统提供了理论和实验依据。
提出光纤荧光测量系统的总体方案,设计测量系统的光路部分,其中包括:光源的选择及驱动电路的设计,滤光及光的传输的实现,光耦合方法及结构设计等;设计微弱荧光信号检测方案,其中包括:光电二极管的选择及电路设计,光电倍增管的选择及电路设计,微弱信号检测技方法的实现。
在对荧光法检测海藻和人工神经网络理论进行分析的基础上,应用BP网络进行三种不同门类海藻的识别,并对相应种类海藻进行浓度预测。设计海藻种类识别BP网络及海藻浓度预测BP网络。
对三种不同门类单一海藻的种类识别和浓度预测实验结果进行分析,验证神经网络应用于光纤荧光海藻检测的方法的可行性。
本研究将荧光法和人工神经网络模式识别和参数预测理论相结合应用于光纤海藻测量系统,能对三种不同门类的海藻进行种类识别和浓度测量,具有数据实时采集、处理及结果显示等功能,为海藻状态监测提供了一种新手段。
Phytoplankton in the sea is the main energy transducer and producer in ocean ecological system, and algae are the major marine phytoplankton. By recogning algae and measuring its concentration, we can estimate community structure and energy distribution in the sea area and realize monitoring and governing ocean pollution.
This paper proposes a combination of fluorescence method and BP neural networks to realize single algal species identification and concentration measurement. By analyzing the basic principles and characteristics of fluorescence measurement and algae fluorescence detection mechanism, studying the different types and different concentrations solution of seaweed and characteristics of pigments contained in the absorption spectra, excitation spectra, fluorescence spectral characteristics,extract characteristic parameter for algae species identification and concentration measurement ,provide theoretical and experimental basis for the design of optical fiber fluorescence measurement system.
Propose total designation scheme of the optical fiber fluorescence measurement system,design optical part of the measurement system, including: the choice of light source and drive circuit design, the realization of filter and optical transmission, optical coupling method and structural design, etc.;design weak fluorescent signal detection method, including: the choice of photodiode and circuit design, the choice of photomultiplier tubes and circuit design, the realization of weak signal detection.
Based on analysing the theory of fluorescence detection and artificial neural network, apply BP network to recognize three kinds of algae, and then measure its concentration,design algal species identification BP network and algae concentration predicted BP network
Analysis the categories identification and concentration estimation results of three different kinds of algae, verify the feasibility of this method.
This research applies fluorescence method and artificial neural network pattern recognition and Parameter estimation theory to optical fiber algae monitor system that can realize recognition and concentration estimation of three kind of algae. This system has real time data collection, data processing and results displaying functions. A novel technical means for algae state monitoring is provides by the paper.
引文
1 C. M. Lalli, T. R. Parsons. Biological Oceanography: An Introduction.Pergamon Press,1993:45-50
2金海龙,王玉田.基于荧光法的活体海藻识别方法研究.光学技术,2006,32(4):381-383
3王世忠,李继刚,高志,等.海水藻类和悬浮物浓度测量仪应用研究.海洋技术,2001,20(1):97-103
4张滨,乔然.赤潮.海洋预报,1994,(3):31
5王文翰,杨坤.保护海洋生态促进海洋产业经济持续发展.东北财经大学学报,2001,13 (1):23-24
6王岩峰,张杰,孙培光,于衍桂,等.用于海洋现场监测的小型叶绿素a荧光计和浊度计.海洋技术,2007,26(1):29-32
7贺岩,吴东.机载海洋激光雷达测量叶绿素a浓度、悬移质浓度和浅海深度的性能估计.中国海洋大学学报,2004,34(4):649-654.
8 C. J. Lorenzen. A Method for the Continuous Measurement of in vivo Chlorophyll-a Concentration. Deep Sea Res,1966,(13):223-227
9夏达英,王振先,张士魁.荧光技术在海洋环境学上的应用研究.海洋学报,1999,21(3):66-71
10 U. Schreiber. Chlorophll Fluorescence Yield Changes as a Tool in Plant Physiology. Photosynthesis Res,1984,4:361-370
11 H. K. Lichtenthaler, U. Rinderle. The Role of Chlorophyll Fluorescence in the Detection of Stress Condition in Plants. CRC Critical Reviews in Analytical Chemistry,1988:29-85
12 Wang Hong,Tang Yu.Development of a Miniaturized Laser-Induced Fluorescence Optical Fiber Detector.Journal of Opt elect ronics laser,2006 ,10(17):175-176
13 H. K. Hongsuk. New Algae Mapping Technique by the Use of an Airborne Laser Fluorescence. Applied Optics,1973,12(7):1454-1459
14 C. S. Yentsch, C. M. Yentsch. Fluorescence Spectral Signatures: The Characterizati- onof Phytoplankton Populations by Theuse of Excitation and Emission Spectra. J. Mar. Res,1979,37:471-483
15 J. S. Cleveland, M. J. Perry. Quantum Yield, Relative Specific Absorption and Fluo- rescence in Nitrogenlimited Chaetoceros Grasilis. Mar. Biol,1987,94:489-497
16 J. B. SooHoo, D. A. Kiefer, D. J. Collins et al. In vivo Fluorescence Excitation and Absorption Spectra of Marine Phytoplankton: I. Taxonomic Characteristics and Responses to Photoadaptation. J. Plankton. Res,1986,8:197-214
17 E. Sakeshaug, G. Johnsen, K. Andresen et al. Modeling of light-dependent Algal Photosynthesis and Growth: Experiments with Barents Sea Diatoms Thalassiosira Nordenskioedii and Chaetoceros Furcellatus. Deep Sea. Res,1991,38:415-430
18 C. S. Yentsch, D. A. Phinney. Spectral Fluorescence: an Ataxonomic Tool for Study- ing the Structure of Phytoplankton Populations. J. Plankton. Res,1985,7:617-632
19 P. B. Oldham, E. J. Zillioux, I. M. Warner. Spectral‘Fingerprinting’of Phytoplankt- on Populations by Two-dimensional Fluorescence and Fourier- transform-based Pattern Recognition. J. mar. Res,1985,43:893-906.
20 T. J. Cowles, R. A. Desiderio, S. Neuer. In situ Characterization of Phytoplankton from Vertical Profiles of Fluorescence Emission Spectra. Mar. Biol,1993,115: 217-222
21 L. Poryvkina, S. Babichenko, S. Kaitala et al. Spectral Fluorescence Signature in the Characterization of Phytoplankton Community Composition. J. Plankton. Res,1994, 16:1315-1327
22 A. M. Chekalyuk, D. K. Bunin, A. V. Chechendaev et al. Lidar Mapping of chloroph- yll-a and Organic Matter Distributions in the Russian Coastal zone of the Black Sea. SPIE,1992,(1922):384-390
23 A. M. Chekalyuk, M. Yu. Gorbunov. Laser remote Sensing of Phytoplankton photos- ynthetic Activity and Chlorophyll by Using a Shipboard Lidar System. SPIE,1992, (1922):391-400
24 M. Yu. Gorbunov, A. M. Chekalyuk. Lidar in-situ Study of Sunlight Regulation of Phytoplankton photosynthetic Activity and Chlorophyll Fluorescence. SPIE,1992, (1922):421-427
25 B. Ruth. Monitoring the Herbicide Concentration in Ponds by Detecting the Chloro- phyll Fluorescence of Algae with ms-Laser-Excitation. SPIE,1995,(2586):46-55
26 D. Mckee, A. Cunningham, K. Jones. An integrated submersible Fluorometer/Nephe- lometer/Transmissometer: Design and Testing at Sea. Optics and Laser Technoloty, 1997,29(1):35-39
27 J. J. Cullen, A. M. ciotti, R. F. Davis. The Relationship between Near-surface Chlor- ophyll and Solar-stimulated Fluorescence: Biological Effects. SPIE,1997, (2963):272-277
28 K. Wild-Allen, P. Tett, D. Bowers. Observations of Diffuse Upwelling Irradiance and Chlorophyll in Case I Waters near the Canary Islands(Spain). Optics and Laser Technology,1997,29(1):3-8
29 M. Beutler, K. H. Wiltshire, B. Meyer et al. A fluorometric Method for the Different- iation of Algal Populations in vivo and in situ. Photosynthesis Research,2002,72 (1):39-53
30潘德炉.利用荧光高度遥感海洋中叶绿素a的浓度.海洋学报,1989,11(6):780-787
31金海龙,汪翔,王玉田.便携式全光纤海藻叶绿素a浓度测量仪的研究.自动化与仪表,2004,(1):23-251
32唐宇,王洪,邓诚先,等.基于光纤束传感器结构的激光诱导荧光检测系统的研制.传感技术学报,2007,20(12):2713-2715
33 E. J. D’sa. Time Series Measurements of Chlorophyll Fluorescence in Oceanic Bott- oBoundary Layer with Multisensor Fiber Optic Fluorometer. J. Atmos, Ocean, Technol,1997,14(4):101-106
34 M. Lehaitre. Optical Fiber Spectrphotometry for in situ Seawater Biological and ChemAnalysis. J. Mar. Environ. Eng,1997,4(1):89-97
35 AllsopT,FloreaniF, JedrzejewskiK, eta.l Refractive index sensing with long-period grating fabricated in biconical tapered fibre.Electron Lett,2005,47(8):471-472
36 S. E. Lohrenz. Development of a multi-Sensor in situ Fiber Optic Fluorometer. Prog- rReport,1992,(1):101-106
37 IadiciccoA,CusanoA,Campopiano S,et a.l Thinned fiberBragg gratings as refractiveindex sensors.IEEE Sensors J, 2005,6(5):1288-1294.
38杜华英,赵跃龙.人工神经网络典型模型的比较研究.计算机技术与发展,2006,16(5):97-99.
39 D. O. Hebb. The Organization of Behavior. New York: Wiley,1949:1-335
40 F. Rosenblatt. The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. Cornell Aeronautical Laboratory: Psychological Review,1958,65(6):386-408
41 B. Widrow, M. E. Hoff. Adaptive Switching Circuits. New York: IRE WESCON Convention Record,1960:96-104
42贾丽会,张修如.BP算法分析与改进.计算机技术与发展,2006,16(10):200-203.
43 J. L. McClelland, D. E. Rumelhart, the PDP Research Group. Parallel Distributed Processing: Psychological and Biological Models. Cambridge, MA: MIT Press,1986: 1-109
44 R. Miikkulainen. Dyslexic and category-specific aphasic impairments in a self-orga- nizing feature map models of the lexicon. Brain and Language,1997,59:334-366
45 S. Grossberg. The Complementary Brain: Unifying Brain Dynamics and Modularity. Trends in Cognitive Science,2000,4(5):233-246
46 E. Oja, M. Kuusela. The ALSM algorithm-an improved subspace method of classif- ication. Pattern Recognition,1983,16(4):421-427
47 L. Xu, A. Krzyzak, E. Oja. A Neural Net for Dual Subspace Pattern Recognition Me- thods. International Journal of Neural Systems,1991,12(3):169-184
48 J. J. Hopfield. Neural Networks and Physical Systems with Emergent Collective Computational Abilities. Proceedings of the National Academy of Sciences, USA, 1982,79:2554-2558
49周开利,康耀红.神经网络模型及其MATLAB仿真程序设计.北京:清华大学出版社,2006:69-100
50 L. O. Chua, L. Yang. Cellular Neural Networks: Theory. IEEE Transactions on Circ- uits and Systems,1988,35(10):1257-1272
51 Cheng Ming-Yang,Wu Hung-Wen,Su A Wen-Yu.On Non-Uniform Rational B-splineSurface Neural Networks.Neural Process Lett,2008(28):1-15.
52 Ruan Qing,Wang Yi-qiang.PCA Approach to BP Learning.Journal of Fudan University:Natural Science,2005,44(2):318-322.
53 A. Sharma, S. G. Schulman. Introduction to Fluorescence Spectroscopy. New York: John Wiley and Sons, Inc,1999:22-23
54 C. L. Catharina. Fluorescence Quenching in Four Unicellar Algae with Diffenert Light-harvesting and Xanthophylls-cycle Pigments. Photosynthesis Research,1998, 56:277-289
55 M. Balode. The Use of spectral Fluorescence Methods to Detect Changes in the Phytoplankton Community. Hydrobiologia,1998,363:207-217
56陈峰,姜锐.微藻生物技术.北京:中国轻工业出版社,1999:1-36
57 http://www.comm-tec.com/Library/pdfs/Tutorials/CTD/FAQ%20Fluorometric%20Chlorophyll%20Analysis. pdf
58 http://www.biologie.uni-hamburg.de/ b-online/ge24/03.htm
59温少红,徐春野,鞠宝,等.紫球藻藻红蛋白的分离纯化及光谱特性.海洋通报,2000,19(6):90-94
60王勇,钱凯先,董强.高纯度藻蓝蛋白分离纯化及光谱特性研究.生物化学与生物物理进展,1999,26(5):457-460
61 R. Aguirre-Gomez, S. R. Boxall, A. R. Weeks. Detecting photosynthetic Algal Pigm- ents in natural Populations Using a high-spectral-resolution Spectroradiometer. International Journal of Remote Sensing,2001,22(15):2867-2884
62邱丽氚,凌元洁,谢树莲.大螺旋藻一些生理特性的研究.木本植物研究,1998,18(3):356-360
63 http://www.comm-tec.com/Library/pdfs/Tutorials/CTD/FAQ%20Fluorometric%20Chlorophyll%20Analysis. pdf
64 D. S. Knowles, S. H. Lieberman. Field Results from the SCAPS Laser-induced Fluo- rescence (LIF) Sensor for in-situ Subsurface Detection of Petroleum Hydrocarb- ons. SPIE,1995,(2504):297-307
65刘君华,贾惠芹,丁晖.虚拟仪器图形化编程语言LabVIEW教程.北京:电子工业出版社,2001:1-5
66 G. W. Gohson. LabVIEW Graphic Programming.USA: MC Graw-Hill,1998:232~133
2金海龙,王玉田.基于荧光法的活体海藻识别方法研究.光学技术,2006,32(4):381-383
3王世忠,李继刚,高志,等.海水藻类和悬浮物浓度测量仪应用研究.海洋技术,2001,20(1):97-103
4张滨,乔然.赤潮.海洋预报,1994,(3):31
5王文翰,杨坤.保护海洋生态促进海洋产业经济持续发展.东北财经大学学报,2001,13 (1):23-24
6王岩峰,张杰,孙培光,于衍桂,等.用于海洋现场监测的小型叶绿素a荧光计和浊度计.海洋技术,2007,26(1):29-32
7贺岩,吴东.机载海洋激光雷达测量叶绿素a浓度、悬移质浓度和浅海深度的性能估计.中国海洋大学学报,2004,34(4):649-654.
8 C. J. Lorenzen. A Method for the Continuous Measurement of in vivo Chlorophyll-a Concentration. Deep Sea Res,1966,(13):223-227
9夏达英,王振先,张士魁.荧光技术在海洋环境学上的应用研究.海洋学报,1999,21(3):66-71
10 U. Schreiber. Chlorophll Fluorescence Yield Changes as a Tool in Plant Physiology. Photosynthesis Res,1984,4:361-370
11 H. K. Lichtenthaler, U. Rinderle. The Role of Chlorophyll Fluorescence in the Detection of Stress Condition in Plants. CRC Critical Reviews in Analytical Chemistry,1988:29-85
12 Wang Hong,Tang Yu.Development of a Miniaturized Laser-Induced Fluorescence Optical Fiber Detector.Journal of Opt elect ronics laser,2006 ,10(17):175-176
13 H. K. Hongsuk. New Algae Mapping Technique by the Use of an Airborne Laser Fluorescence. Applied Optics,1973,12(7):1454-1459
14 C. S. Yentsch, C. M. Yentsch. Fluorescence Spectral Signatures: The Characterizati- onof Phytoplankton Populations by Theuse of Excitation and Emission Spectra. J. Mar. Res,1979,37:471-483
15 J. S. Cleveland, M. J. Perry. Quantum Yield, Relative Specific Absorption and Fluo- rescence in Nitrogenlimited Chaetoceros Grasilis. Mar. Biol,1987,94:489-497
16 J. B. SooHoo, D. A. Kiefer, D. J. Collins et al. In vivo Fluorescence Excitation and Absorption Spectra of Marine Phytoplankton: I. Taxonomic Characteristics and Responses to Photoadaptation. J. Plankton. Res,1986,8:197-214
17 E. Sakeshaug, G. Johnsen, K. Andresen et al. Modeling of light-dependent Algal Photosynthesis and Growth: Experiments with Barents Sea Diatoms Thalassiosira Nordenskioedii and Chaetoceros Furcellatus. Deep Sea. Res,1991,38:415-430
18 C. S. Yentsch, D. A. Phinney. Spectral Fluorescence: an Ataxonomic Tool for Study- ing the Structure of Phytoplankton Populations. J. Plankton. Res,1985,7:617-632
19 P. B. Oldham, E. J. Zillioux, I. M. Warner. Spectral‘Fingerprinting’of Phytoplankt- on Populations by Two-dimensional Fluorescence and Fourier- transform-based Pattern Recognition. J. mar. Res,1985,43:893-906.
20 T. J. Cowles, R. A. Desiderio, S. Neuer. In situ Characterization of Phytoplankton from Vertical Profiles of Fluorescence Emission Spectra. Mar. Biol,1993,115: 217-222
21 L. Poryvkina, S. Babichenko, S. Kaitala et al. Spectral Fluorescence Signature in the Characterization of Phytoplankton Community Composition. J. Plankton. Res,1994, 16:1315-1327
22 A. M. Chekalyuk, D. K. Bunin, A. V. Chechendaev et al. Lidar Mapping of chloroph- yll-a and Organic Matter Distributions in the Russian Coastal zone of the Black Sea. SPIE,1992,(1922):384-390
23 A. M. Chekalyuk, M. Yu. Gorbunov. Laser remote Sensing of Phytoplankton photos- ynthetic Activity and Chlorophyll by Using a Shipboard Lidar System. SPIE,1992, (1922):391-400
24 M. Yu. Gorbunov, A. M. Chekalyuk. Lidar in-situ Study of Sunlight Regulation of Phytoplankton photosynthetic Activity and Chlorophyll Fluorescence. SPIE,1992, (1922):421-427
25 B. Ruth. Monitoring the Herbicide Concentration in Ponds by Detecting the Chloro- phyll Fluorescence of Algae with ms-Laser-Excitation. SPIE,1995,(2586):46-55
26 D. Mckee, A. Cunningham, K. Jones. An integrated submersible Fluorometer/Nephe- lometer/Transmissometer: Design and Testing at Sea. Optics and Laser Technoloty, 1997,29(1):35-39
27 J. J. Cullen, A. M. ciotti, R. F. Davis. The Relationship between Near-surface Chlor- ophyll and Solar-stimulated Fluorescence: Biological Effects. SPIE,1997, (2963):272-277
28 K. Wild-Allen, P. Tett, D. Bowers. Observations of Diffuse Upwelling Irradiance and Chlorophyll in Case I Waters near the Canary Islands(Spain). Optics and Laser Technology,1997,29(1):3-8
29 M. Beutler, K. H. Wiltshire, B. Meyer et al. A fluorometric Method for the Different- iation of Algal Populations in vivo and in situ. Photosynthesis Research,2002,72 (1):39-53
30潘德炉.利用荧光高度遥感海洋中叶绿素a的浓度.海洋学报,1989,11(6):780-787
31金海龙,汪翔,王玉田.便携式全光纤海藻叶绿素a浓度测量仪的研究.自动化与仪表,2004,(1):23-251
32唐宇,王洪,邓诚先,等.基于光纤束传感器结构的激光诱导荧光检测系统的研制.传感技术学报,2007,20(12):2713-2715
33 E. J. D’sa. Time Series Measurements of Chlorophyll Fluorescence in Oceanic Bott- oBoundary Layer with Multisensor Fiber Optic Fluorometer. J. Atmos, Ocean, Technol,1997,14(4):101-106
34 M. Lehaitre. Optical Fiber Spectrphotometry for in situ Seawater Biological and ChemAnalysis. J. Mar. Environ. Eng,1997,4(1):89-97
35 AllsopT,FloreaniF, JedrzejewskiK, eta.l Refractive index sensing with long-period grating fabricated in biconical tapered fibre.Electron Lett,2005,47(8):471-472
36 S. E. Lohrenz. Development of a multi-Sensor in situ Fiber Optic Fluorometer. Prog- rReport,1992,(1):101-106
37 IadiciccoA,CusanoA,Campopiano S,et a.l Thinned fiberBragg gratings as refractiveindex sensors.IEEE Sensors J, 2005,6(5):1288-1294.
38杜华英,赵跃龙.人工神经网络典型模型的比较研究.计算机技术与发展,2006,16(5):97-99.
39 D. O. Hebb. The Organization of Behavior. New York: Wiley,1949:1-335
40 F. Rosenblatt. The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. Cornell Aeronautical Laboratory: Psychological Review,1958,65(6):386-408
41 B. Widrow, M. E. Hoff. Adaptive Switching Circuits. New York: IRE WESCON Convention Record,1960:96-104
42贾丽会,张修如.BP算法分析与改进.计算机技术与发展,2006,16(10):200-203.
43 J. L. McClelland, D. E. Rumelhart, the PDP Research Group. Parallel Distributed Processing: Psychological and Biological Models. Cambridge, MA: MIT Press,1986: 1-109
44 R. Miikkulainen. Dyslexic and category-specific aphasic impairments in a self-orga- nizing feature map models of the lexicon. Brain and Language,1997,59:334-366
45 S. Grossberg. The Complementary Brain: Unifying Brain Dynamics and Modularity. Trends in Cognitive Science,2000,4(5):233-246
46 E. Oja, M. Kuusela. The ALSM algorithm-an improved subspace method of classif- ication. Pattern Recognition,1983,16(4):421-427
47 L. Xu, A. Krzyzak, E. Oja. A Neural Net for Dual Subspace Pattern Recognition Me- thods. International Journal of Neural Systems,1991,12(3):169-184
48 J. J. Hopfield. Neural Networks and Physical Systems with Emergent Collective Computational Abilities. Proceedings of the National Academy of Sciences, USA, 1982,79:2554-2558
49周开利,康耀红.神经网络模型及其MATLAB仿真程序设计.北京:清华大学出版社,2006:69-100
50 L. O. Chua, L. Yang. Cellular Neural Networks: Theory. IEEE Transactions on Circ- uits and Systems,1988,35(10):1257-1272
51 Cheng Ming-Yang,Wu Hung-Wen,Su A Wen-Yu.On Non-Uniform Rational B-splineSurface Neural Networks.Neural Process Lett,2008(28):1-15.
52 Ruan Qing,Wang Yi-qiang.PCA Approach to BP Learning.Journal of Fudan University:Natural Science,2005,44(2):318-322.
53 A. Sharma, S. G. Schulman. Introduction to Fluorescence Spectroscopy. New York: John Wiley and Sons, Inc,1999:22-23
54 C. L. Catharina. Fluorescence Quenching in Four Unicellar Algae with Diffenert Light-harvesting and Xanthophylls-cycle Pigments. Photosynthesis Research,1998, 56:277-289
55 M. Balode. The Use of spectral Fluorescence Methods to Detect Changes in the Phytoplankton Community. Hydrobiologia,1998,363:207-217
56陈峰,姜锐.微藻生物技术.北京:中国轻工业出版社,1999:1-36
57 http://www.comm-tec.com/Library/pdfs/Tutorials/CTD/FAQ%20Fluorometric%20Chlorophyll%20Analysis. pdf
58 http://www.biologie.uni-hamburg.de/ b-online/ge24/03.htm
59温少红,徐春野,鞠宝,等.紫球藻藻红蛋白的分离纯化及光谱特性.海洋通报,2000,19(6):90-94
60王勇,钱凯先,董强.高纯度藻蓝蛋白分离纯化及光谱特性研究.生物化学与生物物理进展,1999,26(5):457-460
61 R. Aguirre-Gomez, S. R. Boxall, A. R. Weeks. Detecting photosynthetic Algal Pigm- ents in natural Populations Using a high-spectral-resolution Spectroradiometer. International Journal of Remote Sensing,2001,22(15):2867-2884
62邱丽氚,凌元洁,谢树莲.大螺旋藻一些生理特性的研究.木本植物研究,1998,18(3):356-360
63 http://www.comm-tec.com/Library/pdfs/Tutorials/CTD/FAQ%20Fluorometric%20Chlorophyll%20Analysis. pdf
64 D. S. Knowles, S. H. Lieberman. Field Results from the SCAPS Laser-induced Fluo- rescence (LIF) Sensor for in-situ Subsurface Detection of Petroleum Hydrocarb- ons. SPIE,1995,(2504):297-307
65刘君华,贾惠芹,丁晖.虚拟仪器图形化编程语言LabVIEW教程.北京:电子工业出版社,2001:1-5
66 G. W. Gohson. LabVIEW Graphic Programming.USA: MC Graw-Hill,1998:232~133