ANSER: ADAPTIVE NEURON ARTIFICIAL NEURAL NETWORK SYSTEM FOR ESTIMATING RAINFALL

M. Zhang, S. Xu, and J. Fulcher

References

  1. [1] G. Cybenko, Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals, and Systems, 2(4), 1989, 303–314.
  2. [2] E. Blum & K. Li, Approximation theory and feedforward networks, Neural Networks, 4, 1991, 511–515. doi:10.1016/0893-6080(91)90047-9
  3. [3] K. Hornik, M. White, & H. Stinchcombe, Multilayer feedforward networks are universal approximators, Neural Networks, 2, 1989, 359–366. doi:10.1016/0893-6080(89)90020-8
  4. [4] M. Leshno, V.Y. Lin, A. Pinkus, & S. Schocken, Multilayer feedforward networks with a nonpolynomial activation function can approximate any function, Neural Networks, 6, 1993, 861–867. doi:10.1016/S0893-6080(05)80131-5
  5. [5] J. Park & I.W. Sandberg, Universal approximation using radial-basis-function networks, Neural Computation, 3, 1991, 246–257. doi:10.1162/neco.1991.3.2.246
  6. [6] F. Scarselli & A.C. Tsoi, Universal approximation using feed-forward neural networks: A survey of some existing methods,and some new results, Neural Networks, 11(1), 1998, 15–37.
  7. [7] M. Zhang, S. Xu, & J. Fulcher, Neuron-adaptive higher order neural network models for automated financial data modelling, IEEEE Trans. on Neural Networks, 13(1), 2002, 188–204. doi:10.1109/72.977302
  8. [8] M. Arai, R. Kohon, & H. Imai, Adaptive control of a neural network with a variable function of a unit and its application, Trans. on Institution Electronic Information Communication, J74-A, 1991, 551–559.
  9. [9] Z. Hu & H. Shao, The study of neural network adaptive control systems, Control and Decision, 7, 1992, 361–366.
  10. [10] T. Yamada & T. Yabuta, Remarks on a neural network controller which uses an auto-tuning method for nonlinear functions, IJCNN, 2, 1992, 775–780.
  11. [11] T. Chen & H. Chen, Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks, IEEE Transactions on Neural Networks, 6(4), 1995, 904–910.
  12. [12] P. Campolucci, F. Capparelli, S. Guarnieri, F. Piazza, & A. Uncini, Neural networks with adaptive spline activation function, Proc. IEEE MELECON 96, Bari, Italy, 1996, 1442–1445.
  13. [13] N.S. Philip & K.B. Joseph, A neural network tool for analysing trends in rainfall, Computers & Geosciences Journal, 29, 2003, 215–223. doi:10.1016/S0098-3004(02)00117-6
  14. [14] C.T. Chen & W.D. Chang, A feedforward neural network with function shape autotuning, Neural Networks, 9(4), 1996, 627–641. doi:10.1016/0893-6080(96)00006-8
  15. [15] T. Chen & H. Chen, Approximations of continuous functionals by neural networks with application to dynamic systems, IEEE Transactions on Neural Networks, 4(6), 1993, 910–918. doi:10.1109/72.286886
  16. [16] M. Zhang & R.A. Scofield, Artificial neural network techniques for estimating heavy convective rainfall and recognition cloud mergers from satellite data, International Journal of Remote Sensing, 15(16), 1994, 3241–3262. doi:10.1080/01431169408954324
  17. [17] K.A. Browning, Airflow and precipitation trajectories within severe local storms which travel to the right of the winds, Journal of Atmospheric Science, 21, 1964, 634–639. doi:10.1175/1520-0469(1964)021<0634:AAPTWS>2.0.CO;2
  18. [18] F.A. Huff, Rainfall gradients in warm season rainfall, Journal of Applied Meteorology, 6, 1967, 435–437. doi:10.1175/1520-0450(1967)006<0052:TAOREO>2.0.CO;2
  19. [19] W.E. Shenk, Cloud top height visibility in strong convective cells, Journal of Applied Meteorology, 13, 1974, 917–922. doi:10.1175/1520-0450(1974)013<0917:CTHVOS>2.0.CO;2
  20. [20] R.J. Kane, C.R. Chelius, & J.M. Fritsch, The precipitation characteristics of mesoscale convective weather system, Journal of Climate & Applied Meteorology, 26(10), 1987, 1323–1335.
  21. [21] C.A. Leary & E.N. Rappaport, The life cycle and internal structure of a mesoscale convective complex, Monthly Weather Review, 115(8), 1987, 1503–1527. doi:10.1175/1520-0493(1987)115<1503:TLCAIS>2.0.CO;2
  22. [22] E.C. Barrett & D.W. Martin, The use of satellite data in rainfall monitoring (New York: Academic Press, 1981).
  23. [23] J. Bullas, J.C. McLeod, & B. de Lorenzis, Knowledge Augmented Severe Storms Predictor (KASSPr): An operationaltest, Proc. 16th Conf. on Severe Local Storms, American Meteorology Society, Boston, 1990, 106–111.
  24. [24] M. Zhang, J. Fulcher, & R. Scofield, Rainfall estimation using artificial neural network group, Neurocomputing, 16(2), 1997, 97–115. doi:10.1016/S0925-2312(96)00022-7
  25. [25] M. Zhang & J. Fulcher, Face recognition using artificial neural network group-based adaptive tolerance (GAT) trees, IEEE Transactions on Neural Networks, 7(3), 1996, 555–567. doi:10.1109/72.501715
  26. [26] M. Zhang, J.C. Zhang, & J. Fulcher, Neural network group models for data approximation, International Journal of Neural Systems, 10(2), 2000, 123–142.
  27. [27] M. Zhang & J. Fulcher, Higher order neural networks for satellite weather prediction, in J. Fulcher & L. Jain (eds.), Applied intelligent systems: New directions (Berlin: Springer, 2004).
  28. [28] J. Fulcher, M. Zhang, & S. Xu, The application of higher-order neural networks to financial time series, in J. Kamruzzaman, R. Begg, & R. Sarker (eds.), Artificial neural networks in finance and manufacturing (Hershey, PA: Idea Group, 2006).
  29. [29] R.A. Scofield, The NESDIS operational convective precipitation estimation technique, Monthly Weather Review, 115, 1987, 1773–1792. doi:10.1175/1520-0493(1987)115<1773:TNOCPE>2.0.CO;2
  30. [30] J. Xie & R.A. Scofield, Satellite-derived rainfall estimates and propagation characteristics associated with mesoscal convective system (MCSs), NAOO Technical Memorandum NESDIS, 25, 1989, 1–49.

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