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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/35271
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/35271


    Title: A Mathematical Study of the Rule Extraction of a 3-layered Feed-forward Neural Networks
    Authors: 林志忠
    Lin, Chih-chung
    Contributors: 蔡瑞煌
    Tsaih, Ray
    林志忠
    Lin, Chih-chung
    Keywords: 類神經網路
    法則萃取
    反函數
    neural networks
    rule-extraction
    inversion function
    Date: 2004
    Issue Date: 2009-09-18 14:36:15 (UTC+8)
    Abstract: 對於神經網路系統將提出一個法則萃取的方式,並從神經網路中得到相關法則。在這裡我們所提到的方法是根據反函數的觀念而得到的。
    A rule-extraction method of the layered feed-forward neural networks is proposed here for identifying the rules suggested in the network. The method that we propose for the trained layered feed-forward neural network is based on the inversion of the functions computed by each layer of the network. The new rule-extraction method back-propagates regions from the output layer back to the input layer, and we hope that the method can be used further to deal with the predicament of ANN being a black box.
    Reference: [1] Andrews, R., Diederich, J., and Tickle, A. (1995). “A survey and critique of techniques for extracting rules from trained artificial neural networks.” Knowledge-Based System, Vol. 8, Issue 6, pp. 373 -389.
    [2] Zhou, R. R., Chen, S. F., and Chen, Z. Q. (2000). “A statistics based approach for extracting priority rules from trained neural networks.” In: Proceedings of the IEEE-INNS-ENNS International Join Conference on Neural Network, Como, Italy, Vol. 3, pp. 401 -406.
    [3] Thrun, S. B., and Linden, A. (1990). “Inversion in time.” In: Proceedings of the EURASIP Workshop on Neural Networks, Sesimbra, Portugal.
    [4] Ke, W. C. (2003). “The Rule Extraction from Multi-layer Feed-forward Neural Networks.” Taiwan: National Chengchi University.
    [5] Tsaih, R., and Lin, C. C. (2004). “The Layered Feed-Forward Neural Networks and Its Rule Extraction.” In: Proceeding of ISNN 2004 International Symposium on Neural Networks, Dalian, China, pp. 377 -382.
    [6] Rumelhart, D. E., Hinton, G. E., and Williams, R., “Learning internal representation by error propagation,” in Parallel Distributed Processing, vol. 1, Cambridge, MA: MIT Press, 1986, pp. 318-362.
    [7] Tsaih, R. (1998). “An Explanation of Reasoning Neural Networks.” Mathematical and Computer Modeling, vol. 28, pp. 37 -44.
    [8] Maire, F. (1999). “Rule-extraction by backpropagation of polyhedra.” Neural Networks, vol. 12, pp. 717 -725.
    Description: 碩士
    國立政治大學
    資訊管理研究所
    92356014
    93
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0923560141
    Data Type: thesis
    Appears in Collections:[資訊管理學系] 學位論文

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