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Intelligent Identification of Ocean Parameters based on RBF Neural Networks

Volume 14, Number 2, February 2018, pp. 269-279
DOI: 10.23940/ijpe.18.02.p8.269279

Li Yuana, Wei Wub, Chuan Tianb, Wei Songb, Xinghui Caob, Lixin Liub

aDepartment of Physical Science, Hainan Medical University, Haikou, 571179, China
bInstitute of Deep-sea Science and Engineering, Chinese Academy of Sciences, Sanya, 572000, China



Abstract:

Ocean data assimilation is challenging because of interactive marine environmental parameters that are affected by macroscopic ocean dynamics. In order to overcome these challenges, a multi-variable assimilation scheme based on a Radial Basis Function (RBF) Neural Network is proposed in this paper. Relative influential parameters are considered as bounded time series variables so that they can be selected for nonlinear function approximating in the first stage. Then, a RBF Neural Network identification model is designed to simulate multiple interactive high-dimensional variables. This simulation is performed by applying proper hidden neurons. According to experimental results, this training method successfully approximates real circumstances. The identification accuracy and vibration are well constricted in the margin evaluated by 1.6×10-5.

 

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