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Title動態徑向基底函數網路與混沌預測
Dynamical Radial Basis Function Networks and Chaotic Forecasting
Creator蔡炎龍
Tsai, Yen Lung
Contributor劉文卿
Liu, Wen Tsin
蔡炎龍
Tsai, Yen Lung
Key Words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
Date1993
Date Issued29-Apr-2016 16:32:37 (UTC+8)
Summary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
參考文獻 [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
資料來源 http://thesis.lib.nccu.edu.tw/record/#B2002004242
Typethesis
dc.contributor.advisor 劉文卿zh_TW
dc.contributor.advisor Liu, Wen Tsinen_US
dc.contributor.author (Authors) 蔡炎龍zh_TW
dc.contributor.author (Authors) Tsai, Yen Lungen_US
dc.creator (作者) 蔡炎龍zh_TW
dc.creator (作者) Tsai, Yen Lungen_US
dc.date (日期) 1993en_US
dc.date.accessioned 29-Apr-2016 16:32:37 (UTC+8)-
dc.date.available 29-Apr-2016 16:32:37 (UTC+8)-
dc.date.issued (上傳時間) 29-Apr-2016 16:32:37 (UTC+8)-
dc.identifier (Other Identifiers) B2002004242en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/88744-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 應用數學系zh_TW
dc.description (描述) 80155012zh_TW
dc.description.abstract (摘要) 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zh_TW
dc.description.abstract (摘要) The forecasting technique is important for many researches anden_US
dc.description.tableofcontents Abstract i
     List of Figures iv
     List of Tables v
     Section 1 introduction 1
     1.1 Background.................................................................................................................1
     1.2 DRBF Networks...........................................................................................................2
     1.3 The Structure of This Paper.........................................................................................3
     
     Section 2 Previous Research 4
      2.1 The Approximation of Functions.................................................................................4
      2.2 Feedforward Networks.................................................................................................5
      2.3 Back Propagation Networks.........................................................................................6
      2.4 Forecasting....................................................................................................................7
     
     Section 3 Dynamical Radial Basis Function Networks 9
      3.1 RBF Networks..............................................................................................................9
      3.2 DRBF Networks.........................................................................................................12
      3.3 Changing Widths........................................................................................................14
     
     Section 4 Experiments 16
      4.1 f(x)=4x(1-x)................................................................................................................17
      4.2 f(x)=sin(-πx).............................................................................................................19
      4.3 AR(1)..........................................................................................................................21
      4.4 MA(1).........................................................................................................................23
      4.5 Sunspots......................................................................................................................25
      4.6 Discussion...................................................................................................................27
     
     Section 5 Conclusion 28
     Reference 29		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#B2002004242en_US
dc.subject (關鍵詞) 神經網路zh_TW
dc.subject (關鍵詞) 徑向基底函數zh_TW
dc.subject (關鍵詞) 函數逼近zh_TW
dc.subject (關鍵詞) 混沌預測zh_TW
dc.subject (關鍵詞) neural networksen_US
dc.subject (關鍵詞) radial basis functionsen_US
dc.subject (關鍵詞) chaotic forecastingen_US
dc.title (題名) 動態徑向基底函數網路與混沌預測zh_TW
dc.title (題名) Dynamical Radial Basis Function Networks and Chaotic Forecastingen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [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.		zh_TW