文献类型：会议论文

英文题名：Soft Measurement Modeling Based on Improved Simulated Annealing Neural Network for Sewage Treatment

作者：Tian, Jingwen[1];Gao, Meijuan[1]

通讯作者：Tian, JW[1]

机构：[1]Beijing Union Univ, Dept Automat Control, 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：World Congress on Software Engineering

会议日期：MAY 19-21, 2009

会议地点：Xiamen, PEOPLES R CHINA

语种：英文

外文关键词：soft measurement; modeling; simulated annealing algorithm; neural network; sewage treatment

摘要：Considering the issues that the sewage treatment process is a complicated and nonlinear system, and the key parameters of sewage treatment quality can not be detected on-line, a soft measurement modeling method based on improved simulated annealing neural network (ISANN) is presented in this paper. First the simulated annealing algorithm with the best reserve mechanism is introduced and it is organic combined with Powell algorithm to form improved simulated annealing mixed optimize algorithm, instead of gradient falling algorithm of BP network to train network weight. It can get higher accuracy and faster convergence speed. We construct the network structure. With the ability of strong self-learning and faster convergence of ISANN, 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.