NDLI: Soft Measurement Modeling Based on Improved Simulated Annealing Neural Network for Sewage Treatment

Content Provider	IEEE Xplore Digital Library
Author	Jingwen Tian Meijuan Gao
Copyright Year	2009
Abstract	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.
Starting Page	486
Ending Page	489
File Size	449473
Page Count	4
File Format	PDF
ISBN	9780769535708
DOI	10.1109/WCSE.2009.207
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sewage treatment Time measurement Neural network Modeling Convergence Soft measurement Neural networks Simulated annealing Cities and towns Simulated annealing algorithm Iterative algorithms Software measurement Nonlinear systems Board of Directors
Content Type	Text
Resource Type	Article

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