NDLI: On leaning algorithm and soft sensor model of swage disposal based on process neural network

Content Provider	IEEE Xplore Digital Library
Author	Su Zhen Lian Xiao-Feng Liu Zai-Wen Wang Xiao-Yi
Copyright Year	2010
Description	Author affiliation: College of Computer and Information Engineering, Beijing Technology and Business University, 100048, China (Su Zhen; Lian Xiao-Feng; Liu Zai-Wen; Wang Xiao-Yi)
Abstract	To solve the problem that water quality of sewage disposal process (such as BOD) is difficulty to measure on-line, meanwhile considering the characteristics of sewage disposal process which is related with time. A soft sensor method for water quality of swage disposal based on process neural network (PNN) was proposed in this paper. On the basis of learning algorithm based on orthogonal function basis expansion, in order to improve the learning rate, the function momentum adjustment item was introduced, moreover, genetic algorithm was used to optimize learning rate and realized learning rate adaptive adjustment algorithm. The soft-sensing model was trained and simulated by a lot of observed data, the experimental results show that the method is effective. So it can implement the real-time and close-loop control of sewage disposal process and have a broad perspective in application.
Starting Page	2342
Ending Page	2346
File Size	511701
Page Count	5
File Format	PDF
ISBN	9781424462636
e-ISBN	9787894631046
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Association of Aut
Subject Keyword	Biological system modeling Adaptation model Process control Sewage Disposal Artificial neural networks Genetic Algorithm Soft Sensor Process Neural Network Data models Electronic mail Board of Directors
Content Type	Text
Resource Type	Article

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