文献类型：会议论文

英文题名：Research of sludge compost maturity degree modeling method based on wavelet neural network for sewage treatment

作者：Gao, Meijuan[1,2];Tian, Jingwen[1,2];Jiang, Wei[1];Li, Kai[2]

第一作者：高美娟;Gao, Meijuan

通讯作者：Gao, MJ[1]

机构：[1]Beijing Union Univ, Dept Automat Control, Postfach, Beijing 100101, Peoples R China;[2]Beijing Univ Chem Technol, Sch Informat Sci, Beijing 100029, 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, Postfach, Beijing 100101, 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：International Conference on Life System Modeling and Simulation (LSMS)

会议日期：SEP 14-17, 2007

会议地点：Shanghai, PEOPLES R CHINA

语种：英文

外文关键词：Control nonlinearities - Engineering research - Learning algorithms - Sewage treatment - Wavelet analysis

摘要：Because of the complicated interaction of the sludge compost components, it makes the compost maturity degree judging system appear the non linearity and uncertainty. According to the physical circumstances of sludge compost, a compost maturity degree modeling method based on wavelet neural network is presented. We select the index of compost maturity degree and take high temperature duration, moisture content, volatile solids, the value of fecal bacteria, germination index as the judgment parameters. We adopt a method of reduce the number of the wavelet basic function by analysis the sparse property of sample data, and use the learning algorithm based on gradient descent to A train network. With the ability of strong function approach and fast convergence of wavelet network, the modeling method can truly judge the sludge compost maturity degree by learning the index information of compost maturity degree. The experimental results show that this method is feasible and effective.