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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xiaojing Shen Ming Chen Jiang Yu |
| Copyright Year | 2009 |
| Abstract | Nowadays, the water quality control in intensive aquaculture is mostly based on single factor in china. The interactions among water factors are often ignored. In this paper, a water environment monitoring system based on BP neural networks is presented, which uses multiple water factors to evaluate whether the water environment is suitable for shrimp growth. In the paper, the whole architecture of the monitoring system is firstly introduced. Then, a multi-sensor information fusion algorithm based on BP neural networks is described in detail. Finally, some actual tests for the information fusion algorithm are given. The test results show that our monitoring system works well with high accuracy. |
| Starting Page | 427 |
| Ending Page | 431 |
| File Size | 270490 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424438358 |
| DOI | 10.1109/AICI.2009.294 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-11-07 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Water Artificial neural networks Information fusion Water monitoring Temperature sensors Databases Neural networks Fusion power generation BP neural networks Artificial intelligence Monitoring Computational intelligence Aquaculture |
| Content Type | Text |
| Resource Type | Article |
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