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    政大機構典藏 > 理學院 > 應用數學系 > 學位論文 >  Item 140.119/88744
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/88744


    Title: 動態徑向基底函數網路與混沌預測
    Dynamical Radial Basis Function Networks and Chaotic Forecasting
    Authors: 蔡炎龍
    Tsai, Yen Lung
    Contributors: 劉文卿
    Liu, Wen Tsin
    蔡炎龍
    Tsai, Yen Lung
    Keywords: 神經網路
    徑向基底函數
    函數逼近
    混沌預測
    neural networks
    radial basis functions
    chaotic forecasting
    Date: 1993
    Issue Date: 2016-04-29 16:32:37 (UTC+8)
    Abstract: 在許多的研究和應用之中都需要預測的技巧。本論文中, 我們建構了一個
    The forecasting technique is important for many researches and
    Reference: [1] Bishop, C.(1991). Improving the generalization properties of radial basis function neural networks. Neural Computation, 3, 579-589.
    [2] Broomhead, D. S., & Lowe, D. (1988). Multivariable functional interpolation and adaptive networks. Complex Systems, 2, 321-355.
    [3] Chen, S., Cowan, C. F. N., & Grant, P. M. (1991). Orthogonal least suares learning algorithm for radial basis function networks. IEEE Transcations on Neural Networks, 2, 302-309

    [4] Friedberg, S. H., Insel, A. J., & Spence, L. E. (1989). Linear Algebra. Englewood Cliffs, N.j.: Prentice-Hall, Inc.
    [5] Hartman, E. J., Keeler, J. D., & Kowalski, J. M. (1990). Layered neural networks with Gaussian Hidden units as universal approximations. Neural Computation, 2, 210-219.
    [6] Jones, R. D., Lee, Y. C., Barnes, C. W., Flake, G. W., Lee, K., Lewis, P.S., & Qian, S. (1990). Function approximation and time series prediction with neural networks. Proceedings of International Joint Confernence on Neural Networks, 1, 649-665.
    [7] Lapedes, A. S., & Farber, R. M. (1987). Nonlinear signal processing using neural networks: prediction and system modeling. Technical Report. Los Alamos National Laboratory, Los Alamos, New Mexico.
    [8] May, R. M. And Sugihara, G. (1990). Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature, 344, 734-741.
    [9] Moody J., & Darken, C. J. (1989). Fast learning in networks of locally tuned processing units. Neural Computation, 1, 281-294.
    [10] Musavi, M. T., Ahmed, W., Chan, K. H., Faris, K. B., & Hummels, D. M. (1992). On the training of radial basis function classifiers, Neural Networks. 5,595-603.
    [11] Park, J., & Sandberg, I. W. (1991). Universal approximation using radial-basis-function networks. Neural Computation, 3, 246-257.
    [12] Qian, S., Lee, Y. C., Jones, R. D., Barnes, C. W., & Lee, K. (1990). Function approximation with an orthogonal basis net. Technical Report. Los Alamos National Laboratory, Los Alamos, New Mexico.
    [13] Rasband, S. N. (1990). Chaotic Dynamics of Nonlinear System. New York: John Wiley & Sons, Inc.
    [14] Rice, J. R. (1964). The Approximation of Functions. Reading, Mass: Addison-Wesley Pubblish Company, Inc.
    [15] Rumelhart, D. E., & McClelland, J. L. (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Cambridge, Mass.: MIT Press.
    [16] Weigend, A. S., Huberman, B. A., & Rumelhart, D. E. (1990). Predicting the future: a connectionist approach. International Journal of Neural Systems, 1, 193-209.
    Description: 碩士
    國立政治大學
    應用數學系
    80155012
    Source URI: http://thesis.lib.nccu.edu.tw/record/#B2002004242
    Data Type: thesis
    Appears in Collections:[應用數學系] 學位論文

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