文献类型：会议论文

英文题名：Soft Measurement Modeling Based on High Speed and Precise Genetic Algorithm Neural Network for Sewage Treatment

作者：Gao, Meijuan[1,2];Tian, Jingwen[1,2];Zhang, Fan[2];Wang, Yuping[1]

第一作者：Gao, Meijuan;高美娟

通讯作者：Gao, MJ[1]

机构：[1]Beijing Union Univ, Dept Automat Control, \ Beijing, Peoples R China;[2]Beijing Univ Chem Technol, Sch Informat Sci, Beijing, Peoples R China

第一机构：北京联合大学城市轨道交通与物流学院

通讯机构：[1]corresponding author), Beijing Union Univ, Dept Automat Control, \ Beijing, Peoples R China.|[1141751]北京联合大学城市轨道交通与物流学院;[11417]北京联合大学;

会议论文集：7th World Congress on Intelligent Control and Automation

会议日期：JUN 25-27, 2008

会议地点：Chongqing, PEOPLES R CHINA

语种：英文

外文关键词：Genetic algorithms; Neural networks; Soft measurement; Sewage treatment; Modeling

摘要：Considering the issues that the sewage treatment process is a complicated and nonlinear system, it is very difficult to found the process model to describe it, and the key parameters of sewage treatment quality can not, be detected on-line, a soft measurement modeling method based on high speed and precise genetic algorithm neural network is presented in this paper. The high speed and precise genetic algorithm neural network is combined the adaptive and floating-point code genetic algorithm with BP network which has higher accuracy and faster convergence speed. With the ability of strong self-learning and faster convergence of high speed and precise genetic algorithm neural network, the soft measurement modeling method can truly detect and assess the quality of sewage treatment in real time by learning the sewage treatment parameter information of sensors acquired. The experimental results show that this method is feasible and effective.