NDLI: A Soft-Sensing Approach to On-Line Predicting Ammonia-Nitrogen Based on RBF Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Changhui Deng Deyan Kong Yanhong Song Li Zhou Jun Gu
Copyright Year	2009
Abstract	Measuring ammonia-nitrogen in the aquaculture water is always a problem that how to carry out the on-line monitoring in the process of industrialized culture. There isn’t a more effective method to realize the real time on-line monitoring at present. Some even need expensive instruments and operators having high skills. The normal methods can only be performed in the laboratory, so it can’t be accomplished the requirement of the fast-field evaluation. Because of above factors, the development of industrialized culture in our country is not fast enough. In this paper it is built that the intelligent mathematic model which is used to predicting ammonia-nitrogen in the aquaculture water and which is based on RBF Neural Network (RBF NN). Through comparing the model values with the measured values, we can emend the predicting model the second time to realize the intelligent prediction of ammonia-nitrogen. The results show that the soft-sensing approach to on-line predicting ammonia-nitrogen based on RBF neural network is effective.
Starting Page	454
Ending Page	458
File Size	619395
Page Count	5
File Format	PDF
ISBN	9781424443598
DOI	10.1109/ICESS.2009.44
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Performance evaluation Instruments RBF neural network (RBF NN) Laboratories ammonia-nitrogen Predictive models Mathematics industrialized culture Intelligent networks soft-sensing Neural networks CLS correction Mathematical model Monitoring Aquaculture
Content Type	Text
Resource Type	Article

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