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題名 動態徑向基底函數網路與混沌預測
Dynamical Radial Basis Function Networks and Chaotic Forecasting作者 蔡炎龍
Tsai, Yen Lung貢獻者 劉文卿
Liu, Wen Tsin
蔡炎龍
Tsai, Yen Lung關鍵詞 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日期 1993 上傳時間 29-四月-2016 16:32:37 (UTC+8) 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
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. 描述 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 資料類型 thesis dc.contributor.advisor 劉文卿 zh_TW dc.contributor.advisor Liu, Wen Tsin en_US dc.contributor.author (作者) 蔡炎龍 zh_TW dc.contributor.author (作者) Tsai, Yen Lung en_US dc.creator (作者) 蔡炎龍 zh_TW dc.creator (作者) Tsai, Yen Lung en_US dc.date (日期) 1993 en_US dc.date.accessioned 29-四月-2016 16:32:37 (UTC+8) - dc.date.available 29-四月-2016 16:32:37 (UTC+8) - dc.date.issued (上傳時間) 29-四月-2016 16:32:37 (UTC+8) - dc.identifier (其他 識別碼) B2002004242 en_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) 80155012 zh_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 and en_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/#B2002004242 en_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 networks en_US dc.subject (關鍵詞) radial basis functions en_US dc.subject (關鍵詞) chaotic forecasting en_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 Forecasting en_US dc.type (資料類型) thesis en_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
