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基于RBF网络的光纤荧光海水叶绿素a含量在线监测系统的研究
引用本文:王玉田,汪翔,金海龙. 基于RBF网络的光纤荧光海水叶绿素a含量在线监测系统的研究[J]. 光学与光电技术, 2003, 1(5): 26-29
作者姓名:王玉田  汪翔  金海龙
作者单位:燕山大学电气工程学院,秦皇岛,066004;燕山大学电气工程学院,秦皇岛,066004;燕山大学电气工程学院,秦皇岛,066004
基金项目:霍英东教育基金(71051)
摘    要:介绍了能够实现对海水中叶绿素a含量在线监测的光纤荧光系统。系统将荧光技术、光纤技术和基于RBF(Radial Basis Function Neural Network)神经网络的标定方法相结合,建立了用于海水叶绿素a含量在线监测的最佳RBF网络结构,系统具有结构简单,探头无源及高灵敏度等特点。

关 键 词:荧光技术  光纤技术  RBF神经网络  叶绿素a含量
文章编号:1672-3392(2003)05-26-04
收稿时间:2003-10-05
修稿时间:2003-10-05

Research on Optical-Fiber Fluorescent Inspecting System for Chlorophyll-a Content in Sea-Water Based on RBF Network
WANG Yu-tian WANG Xiang JIN Hai-long. Research on Optical-Fiber Fluorescent Inspecting System for Chlorophyll-a Content in Sea-Water Based on RBF Network[J]. optics&optoelectronic technology, 2003, 1(5): 26-29
Authors:WANG Yu-tian WANG Xiang JIN Hai-long
Abstract:An on-line optical-fiber fluorescence inspecting system for chlorophyll-a in seawater is presented. Fluorescence technology, optical-fiber technology and a calibration method based on RBF(Radial Basis Function Neural Network) neural network are combined, and an optimal RBF network architecture for the on-line inspecting the chlorophyll-a content in sea-water is set up. The system has advantages of simple structure, passive sensor head and high sensitivity.
Keywords:fluorescence technology  optical-fiber technology  RBF neural network  chlorophyll-a content
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