A kind of Soft Sensing Method for Biomass Concentration of Phytoplankton in Seawater
作者单位：College of Information EngineeringShanghai Maritime University
会议名称：《第26届中国控制与决策会议》
会议日期：2014年
学科分类：0810[工学-信息与通信工程] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0835[工学-软件工程] 081002[工学-信号与信息处理]
基 金：supported by National Nature Science Foundation of China under Grant No.61273068 Nature Science Foundation of Shanghai under Grant No.12ZR1412600 Scientific Research Innovation Project of Shanghai Education Committee under Grant No.13YZ084
关 键 词：Soft sensing Extreme learning machine regression Biomass concentration of phytoplankton Secondary variables Generalization ability
摘 要：Effective monitoring the growth state of seawater phytoplankton plays an important role for the early warning of marine disasters, such as coastal red tides. Grey correlation analysis method was used to select the secondary variables of the soft sensing model. It can effectively reduce the dimension of the system. Extreme learning machine regression（ELMR） method was used to build the soft sensing model of biomass concentration of phytoplankton. Comparing with the generalized regression neural network, the testing result indicates that extreme learning machine regression has better accuracy, efficiency and generalization ability of measurement than the other methods. It adapts to be used for real time monitoring of biomass concentration of phytoplankton in seawater.