A Wavelet Neural Network Based Prediction forConductivity of Seawater
文献类型：会议论文
| 作者 | Feiyan Qin; Zhongming Pan; Weimin Li; Haibin Wang and Max Q.-H. Meng |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | 武夷山 |
| 英文摘要 | In order to get the conductivity of seawater in advance to give help to the research on communication technology in ocean, this paper adopts a wavelet neural network based method to establish a prediction model. The input variables of the prediction model are the seawater environmental factors influencing conductivity, which include temperature, pressure, and salinity. The output variable is the seawater conductivity. The prediction model is a three layer wavelet neural network with seven hidden nodes. We get 264 sets data with CTD (conductivity, temperature and depth) analyzer. After data repairing and a wavelet de-noising, the 264 sets data are used for wavelet eural network training and prediction. Simulation results show that the wavelet neural network based method has good performance in conductivity prediction. The new method can be viewed as a new approach to advance the development of prediction model of seawater conductivity and improve the prediction accuracy. |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/13738]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 推荐引用方式 GB/T 7714 | Feiyan Qin,Zhongming Pan,Weimin Li,et al. A Wavelet Neural Network Based Prediction forConductivity of Seawater[C]. 见:. 武夷山. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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