A Soft-sensing Approach to On-line Predicting Ammonia-Nitrogen Based on RBF Neural Networks

被引：3

作者：

Deng, Changhui ^{[1
]}

Kong, Deyan ^{[1
]}

Song, Yanhong ^{[1
]}

Zhou, Li ^{[1
]}

Gu, Jun ^{[1
]}

机构：

[1] Dalian Fisheries Univ, Sch Informat Engn, Dalian 116023, Peoples R China

来源：

2009 INTERNATIONAL CONFERENCE ON EMBEDDED SOFTWARE AND SYSTEMS, PROCEEDINGS | 2009年

关键词：

industrialized culture; ammonia-nitrogen; soft-sensing; RBF neural network (RBF NN); CLS correction;

D O I：

10.1109/ICESS.2009.44

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：454 / 458

页数：5

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