The Construction of Sound Speed Field Based on Back Propagation Neural Network in the Global Ocean

References

• Ai, R. F., J. Cheng, J. Ouyang, and J. Yang. 2015. On-line retrieval methodology for sound speed profile of sea area. Journal of Computer Applications 35 (S1):327-330,338.
   Google Scholar
• Baldacci, A., G. Corsini, R. Grasso, G. Manzella, J. T. Allen, P. Cipollini, T. H. Guymer, and H. M. Snaith. 2001. A study of the Alboran sea mesoscale system by means of empirical orthogonal function decomposition of satellite data. Journal of Marine Systems 29 (1–4):293–311.
   Web of Science ®Google Scholar
• Davis, R. E. 1976. Predictability of sea surface temperature and sea level pressure anomalies over the North Pacific Ocean. Journal of Physical Oceanography 6 (3):249–66.
   Web of Science ®Google Scholar
• Ding, S., C. Su, and J. Yu. 2011. An optimizing BP neural network algorithm based on genetic algorithm. An optimizing BP neural network algorithm based on genetic algorithm.
   Google Scholar
• He, L., J. J. Liu, L. X. Wu, Z. L. Li, and Z. H. Peng. 2011. Inversion for sound speed profiles in the northern of South China Sea. SCIENTIA SINICA Physica, Mechanica & Astronomica 41 (1):49–57.
   Google Scholar
• Huang, C. F., G. Peter, and W. S. Hodgkiss. 2008. Effect of ocean sound speed uncertainty on matched-field geoacoustic inversion. Journal of the Acoustical Society of America 123 (6):162–8.
   Web of Science ®Google Scholar
• Huang, Y., and W. S. Guo. 2014. Spatial correlation of the acoustic vector field of the surface noise in three-dimensional ocean environments. The Journal of the Acoustical Society of America 135 (4):2397.
   Google Scholar
• Ji, C. Y., and L. Cong. 2013. Model experiment of intelligent control for deep water jack-up platforms based on BP neural network. The Ocean Engineering 31 (2):19–27.
   Google Scholar
• Li, H., F. Xu, W. Zhou, D. Wang, J. S. Wright, Z. Liu, and Y. Lin. 2017. Development of a global gridded Argo data set with Barnes successive corrections. Journal of Geophysical Research: Oceans 122 (2):866–89.
   Web of Science ®Google Scholar
• Li, J., J. H. Cheng, and J. Y. Shi. 2012. Brief introduction of back propagation (BP) neural network algorithm and its improvement. In Advances in computer science and information engineering, eds. D. Jin and S. Lin, 553–8. Berlin, Germany: Springer.
   Google Scholar
• Liu, L., J. Chen, and L. Xu. 2008. Realization and application research of BP neural network based on MATLAB. International Seminar on Future BioMedical Information Engineering, Wuhan, Hubei, pp. 130–3.
   Google Scholar
• Luo, Z. H., B. Lu, and Y. Yang. 2009. Application of artificial neural network technology in sound velocity prediction for marine sediments. Ocean Technology 28 (4):41–57.
   Google Scholar
• McClellan, J. L., and D. E. Rumelhart. 1988. Neural network programs (Book Reviews: Explorations in Parallel Distributed Processing). Science 241:1107–8.
   PubMed Web of Science ®Google Scholar
• Munk, W., and C. Wunsch. 1979. Ocean acoustic tomography: A scheme for large scale monitoring. Deep-Sea Research, Part A (Oceanographic Research Papers) 26 (2):123–61.
   Web of Science ®Google Scholar
• Najah, A., A. El-Shafie, O. A. Karim, and A. H. El-Shafie. 2013. Application of artificial neural networks for water quality prediction. Neural Computing and Applications 22 (S1):187–201.
   Web of Science ®Google Scholar
• Park, J. C., and R. M. Kennedy. 1996. Remote sensing of ocean sound speed profiles by a perceptron neural network. IEEE Journal of Oceanic Engineering 21 (2):216–24.
   Web of Science ®Google Scholar
• Roy, R., and T. Kailath. 1989. ESPRIT – Estimation of Signal Parameters via Rotational Invariance Techniques. IEEE Transactions on Acoustics, Speech, and Signal Processing 37 (7):984–95.
   Google Scholar
• Ren, C., N. An, J. Wang, L. Li, B. Hu, and D. Shang. 2014. Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting. Knowledge-Based Systems 56 (3):226–39.
   Web of Science ®Google Scholar
• Rumelhart, D. E., B. Widrow, and M. A. Lehr. 1994. The basic ideas in neural networks. Communications of the ACM 37 (3):87–92.
   Web of Science ®Google Scholar
• Shen, Y., Y. Ma, Q. Tu, and X. Jiang. 2000. Inversion of sound speed profile for shallow-water environment with experimental verification. Journal of Northwestern Polytechnical University.
   Google Scholar
• Tolstoy, A. 1994. Simulated performance of acoustic tomography via matched field processing. Journal of Computational Acoustics 2 (1):1–10.
   Google Scholar
• Worcester, P. F., and W. H. Munk. 2016. Ocean acoustic tomography: Fortieth anniversary, 1976–2016. The Journal of the Acoustical Society of America 140 (4):2976.
   Google Scholar
• Wu, Y. 2013. Study on theory and method of precise LBL positioning and development of positioning software system. Wuhan University.
   Google Scholar
• Yu, Y., Z. Li, and L. He. 2010. Matched-field inversion of sound speed profile in shallow water using a parallel genetic algorithm. Chinese Journal of Oceanology and Limnology 28 (5):1080–5.
   Web of Science ®Google Scholar
• Zhang, Z., J. Bao, F. Xiao, and J. Xin. 2018. Inversion of sound speed profile in multibeam survey based on simulated annealing algorithm. Geomatics & Information Science of Wuhan University.
   Google Scholar
• Zhang, Z. B., Y. L. Ma, K. D. Yang, and S. Yan. 2004. Inversion for sound speed profile in shallow water using matched-beam processing. Chinese Journal of Acoustics (3):259–67.
   Google Scholar
• Zhou, F. N., J. H. Zhao, and C. Y. Zhou. 2001. Determination of classic experiential sound speed formulae in multi-beam echo sounding system. Taiwan Strait 20 (4):411–9.
   Google Scholar