Username   Password       Forgot your password?  Forgot your username? 

ISSUES BY YEAR

Volume 14 - 2018

No.1 January 2018
No.1 January 2018
No.3 March 2018
No.3 March 2018
No.4 April 2018
No.4 April 2018
No.5 May 2018
No.5 May 2018
No.6 June 2018
No.6 June 2018
No.7 July 2018
No.7 July 2018
No.8 August 2018
No.8 August 2018

Volume 13 - 2017

No.4 July 2017
No.4 July 2017
No.5 September 2017
No.5 September 2017
No.7 November 2017
No.7 November 2017
No.8 December 2017
No.8 December 2017

Volume 12 - 2016

Volume 11 - 2015

Volume 10 - 2014

Volume 9 - 2013

Volume 8 - 2012

Volume 7 - 2011

Volume 6 - 2010

Volume 5 - 2009

Volume 4 - 2008

Volume 3 - 2007

Volume 2 - 2006

 

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.

 

References: 28

    1. J.L. Anderson, “Reducing Correlation Sampling Error in Ensemble Kalman Filter Data Assimilation,” Monthly Weather Review, vol. 144, no. 3, pp. 913–925, 2015.
    2. F. Bouttier and P. Courtier, “Data Assimilation Concepts and Methods March 1999,” Meteorological training course lecture series. ECMWF, 2002.
    3. J.S. Brain, Isaac M. Held. “An Assessment of Climate Feedbacks in Coupled Ocean–Atmosphere Models,” Journal of Climate, Vol. 19, pp. 3354-3357, 2006.
    4. L. Bopp, L. Resplandy, et al. “Multiple Stressors of Ocean Ecosystems in the 21st Century: Projections with CMIP5 models,” Biogeosciences, 10, 6225 -6245, 2013.
    5. M. Bocquet, H. Elbern, et al. “Data Assimilation in Atmospheric Chemistry Models: Current Status and Future Prospects for Coupled Chemistry Meteorology Models,” Atmospheric chemistry and physics, vol. 15, no. 10, pp. 5325–5358, 2015.
    6. M. Buehner, J. Morneau, and C. Charette, “Four-dimensional Ensemble-variational Data Assimilation for Global Deterministic Weather Prediction,” Nonlinear Processes in Geophysics, vol. 20, no. 5, pp. 669–682, 2013.
    7. D. Erdal and O. Cirpka, “Joint Inference of Groundwater-recharge and Hydraulic-conductivity Fields from Head Data Using the Ensemble Kalman filter,” Hydrology and Earth System Sciences, vol. 20, no. 1, pp. 555–569, 2016.
    8. C. A. Edwards, A. M. Moore, I. Hoteit, and B. D. Cornuelle, “Regional Ocean Data Assimilation,” Annual review of marine science, vol. 7, pp. 21–42, 2015.
    9. G. Evensen, “Data assimilation: the ensemble Kalman filter,” springer Science & Business Media, 2009.
    10. G. Ferri, M. Cococcioni, and A. Alvarez, “Mission Planning and Decision Support for Underwater Glider Networks: A sampling on-demand approach,” Sensors, vol. 16, no. 1, p. 28, 2015.
    11. S.M. Griffies, Michael Winton et al. “Impacts on Ocean Heat from Transient Mesoscale Eddies in a Hierarchy of Climate Models,” Journal of Climate, Vol. 28, pp. 952-970, 2015.
    12. I. Iermano, A. Moore, and E. Zambianchi, “Impacts of a 4-dimensional Variational Data Assimilation in a Coastal Ocean Model of Southern Tyrrhenian sea,” Journal of Marine Systems, vol. 154, pp. 157–171, 2016.
    13. C. James, C. Gennady, C. Xianhe, and G. Benjamin, “A Simple Ocean Data Assimilation Analysis of the Global Upper Ocean 1950-95. part i: Methodology,” Journal of Physical Oceanography, vol. 30, no. 2, pp.294–309, 2000.
    14. M. Kretschmer, B. R. Hunt, and E. Ott, “Data Assimilation Using a Climatologically Augmented Local Ensemble Transform Kalman filter,” Tellus A, vol. 67, pp. 1–5, 2015.
    15. Z.J. Li, James C. McWilliams, et al. “Coastal Ocean Data Assimilation Using a Multi-scale Three-dimensional Variational Scheme,” Ocean Dynamics, 65(7), 1001-1015, 2015.
    16. Y. Liu, H. Meier, and L. Axell, “Reanalyzing Temperature and Salinity on Decadal Time Scales Using the Ensemble Optimal Interpolation Data Assimilation Method and a 3D Ocean Circulation Model of the Baltic sea,” Journal of Geophysical Research: Oceans, vol. 118, no. 10, pp. 5536–5554, 2013.
    17. S. Mohanty, A. Chattopadhyay, P. Peralta, and S. Das, “Bayesian Statistic Based Multivariate Gaussian Process Approach for Offline/online Fatigue Crack Growth Prediction,” Experimental mechanics, vol. 51, no. 6, pp.833–843, 2011.
    18. P.A. Muscarella, M. Carrier, and H. Ngodock, “An Examination of a Multi-scale Three-dimensional Variational Data Assimilation Scheme in the Kuroshio Extension Using the Naval Coastal Ocean Model,” Continental Shelf Research, vol. 73, pp. 41–48, 2014.
    19. A. Mozaffari, K. A. Scott, S. Chenouri, and N. L. Azad, “A Modular Ridge Randomized Neural Network with Differential Evolutionary Distributor Applied to the Estimation of Sea Ice Thickness,” Soft Computing, pp. 1–25, 2016.
    20. P.R. Oke, G. B. Brassington, D. A. Griffin, and A. Schiller, “The Bluelink Ocean Data Assimilation System (bodas),” Ocean Modelling, vol. 21, no. 1, pp. 46–70, 2008.
    21. Q. Peng and C. Lei., “The Assimilation of Jason-2 Significant Wave Height Data in The North Indian Ocean Using the Ensemble Optimal Interpolation,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 1, pp. 287–297, August 2016.
    22. D. Paiva, R. C., M. T. Durand, and F. Hossain, “Spatiotemporal Interpolation of Discharge Across a River Network by Using Synthetic Swot Satellite Data,” Water Resources Research, vol. 51, no. 1, pp. 430–449, 2015.
    23. S. Suranjana, Moorthi, et al., “The Ncep Climate Forecast System Reanalysis,” Bulletin of the American Meteorological Society, vol. 91, no. 8, pp. 1015–1057, August 2010.
    24. N. Usui, Y. Fujii, K. Sakamoto, and M. Kamachii, “Development of a Four-dimensional Variational Assimilation System for Coastal Data Assimilation around Japan,” Monthly Weather Review, vol. 143, no. 10, pp. 3874–3892, 2015.
    25. C. Ubelmann, P. Klein, and L. L. Fu, “Dynamic Interpolation of Sea Surface Height and Potential Applications for Future High-resolution Altimetry Mapping,” Journal of Atmospheric and Oceanic Technology, vol. 32, no. 1, pp. 177–184, 2015.
    26. C. W. and D. Hill, “Deterministic Learning and Rapid Dynamical Pattern Recognition,” IEEE Transactions on Neural Networks, vol. 18, no. 3, pp. 617–630, 2007.
    27. W.Z. Zhang, M. Hao, M. Snir. Predicting HPC Parallel Program Performance Based on LLVM compiler. Cluster Computing, 20(2), 1179-1192, 2017.
    28. M. Zhang and F. Zhang, “E4dvar: Coupling an Ensemble Kalman filter with Four-dimensional Variational Data Assimilation in a Limited-area Weather Prediction Model,” Monthly Weather Review, vol. 140, no. 2, pp. 587–600, 2012.

       

      Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

      Attachments:
      Download this file (IJPE-2018-02-08.pdf)IJPE-2018-02-08.pdf[Intelligent Identification of Ocean Parameters based on RBF Neural Networks]2032 Kb
       

      CURRENT ISSUE

      Prev Next

      Program Disturb Research and Error Avoidance Algorithm Design of 3D-TLC NAND Flash Memory

      Xiaoshan Yang, Ligu Zhu, Qicong Zhang, Meng Zhang, Fei Wu, and Wei Zhang

      Read more

      Data Complexity Analysis for Software Defect Detection

      Ying Ma, Yichang Li, Junwen Lu, Peng Sun, Yu Sun, and Xiatian Zhu

      Read more

      Fuzzy AHP-based Comprehensive Evaluation for Smart Grid in Energy Internet Systems

      Ying Ma, Yichang Li, Shunzhi Zhu, Nan Qin, Guang Zhao, and Chao Huang

      Read more

      User Group-based Method for Cold-Start Recommendation

      Jing He, Shuo Yuan, Yi Xiang, and Wei Zhou

      Read more

      Object Tracking Method based on 3D Cartoon Animation in Broadcast Soccer Videos

      Chunlong Xie, Zhiqian Zhang, Chunsheng Wang, and Zhengqing Liu

      Read more

      Image Encryption Method based on Hill Matrix and Dynamic DNA Encoding

      Xuncai Zhang, Zheng Zhou, Yishan Liu, Guangzhao Cui, Ying Niu, and Yanfeng Wang

      Read more

      Video Indexing and Retrieval based on Key Frame Extraction

      Wenshi Wang, Zhangqin Huang, Weidong Wang, Shuo Zhang, and Rui Tian

      Read more

      Modeling Approach Combining Performance and Reliability for Mobile Cloud System

      Han Xu, Haiqing Wang, Liang Luo, Xiwei Qiu, Sa Meng, and Xun Guo

      Read more

      Understanding the Similarity of Log Revision Behaviors in Open Source Software

      Xu Niu, Shanshan Li, Zhouyang Jia, Shulin Zhou, Wang Li, and Xiangke Liao

      Read more

      Learning to Predict Price based on E-commerce Online Auction Machine

      Xiaohui Li, Hongbin Dong, Xiaowei Wang, and Shuang Han

      Read more

      Rate Control Algorithm for Multiview Video Coding based on Human Visual Characteristics

      Tao Yan, In-Ho Ra, Qiuwen Zhang, Hui Wen, Hang Xu, and Shuqing Chen

      Read more
      This site uses encryption for transmitting your passwords. ratmilwebsolutions.com