臺灣博碩士論文加值系統

在這篇論文中，針對分類的問題，我們使用一個輔以灰關聯樣本分析之類神經氣體演算法來處理。近年來，有許多有關於類神經氣體演算法的研究。這些研究展現出許多改良類神經演算法效能的進步。在我們的研究，是利用原本的類神經演算法在加上灰關聯樣本分析。此外，並討論了在灰關連樣本分析中區別係數的選擇。訓練的效能是藉由一些資料庫來測試。這些多樣化的訓練資料展現了灰關聯類神經氣體演算法的可用性，以及說明提出的方法可以廣泛使用在各個領域。模擬結果驗證了輔以灰關聯樣本分析之氣體演算法對於分類的成果。

In this thesis, a gray relational pattern analysis based neural gas algorithm for dealing with the problem of classification is proposed. In recent years, there are many researches about the neural gas algorithm that have shown many developments to improve the performances of the original neural gas algorithm. In our study, the neural gas network algorithm is adopted by means of combining the gray relational pattern analysis. Besides, the choice of the distinguishing coefficient in gray relational pattern analysis is discussed. The proposed algorithm is tested by some databases. The training of the varied data sets show the flexibility and performance of the gray relational pattern analysis based neural gas algorithm and illustrate the proposed method can be wildly used in many fields. The simulation results are conducted to verify the effectiveness of the gray relational pattern analysis based neural gas algorithm on the aspect of data clustering.

CHINESE ABSTRACT i
ENGLISH ABSTRACT ii
ACKNOWLEDGEMENTS iii
TABLE OF CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES vii
CHAPTER
1 INTRODUCTION 1
2 LITERARY REVIEWS 6
2.1 The basic description of the Neural Network 6
2.2 The self-organizing map and the neural gas algorithm 11
2.3 Gray relational analysis and Gray relational pattern analysis 17
3 GRAY RELATIONAL PATTERN ANALYSIS BASED NEURAL GAS ALGORITHM 22
3.1 Construction of gray neural gas algorithm 22
3.2 Example and explain 27
3.3 Summary 36
4 SIMULATION RESULTS 37
4.1 Training database 37
4.2 Parameters Design 39
4.3 Testing Results 40
5 CONCLUSION 47
REFERENCES 48

[1]R. Schalkoff, Pattern recognition: statistical, structural, and neural approaches, New York: Wiley, 1992.
[2]J. Y. Haiso and A. A. Sawchuk, “Unsupervised texture image segmentation using feature smoothing and probabilistic relaxation techniques,” Comput. Vis. Graph. Image Process., vol. 48, pp. 1–21, 1989.
[3]R. P. Lippmann, “An introduction to computing with neural nets,” IEEE ASSP Mag, vol. 4, pp. 4–22, 1987.
[4]D. L. Reilly, L. N. Cooper, and C. Elbaum, “A neural model for category learning,” Biol. Cybern., vol. 45, pp. 35–41, 1982.
[5]K. Fukushima, S. Miyake, and T. Ito, “Neocognitron: a neural network model for a mechanism of visual pattern recognition,” IEEE Trans. Syst., Man, Cybern, vol. SMC-13, pp. 826–834, 1983.
[6]D. E. Rumelhart, G. E. Hinton, and R. J. Williarms, “Learning internal representations by error propagation,” in Parallel Distributed Processing, D. E. Rumelhart, J. L. McClelland, and the PDP Research Group, Eds, vol. I. Cambridge, MA: MIT Press, pp. 318–364, 1986.
[7]G. A. Carpenter and S. Grossberg, “A massively parallel architecture for a self-organizing neural pattern recognition machine,” Comput. Vis., Graph. Image Process., vol. 37, pp. 54–115, 1987.
[8]T. Kohonen, Self-organizing maps, 2nd ed. Berlin, Germany: Springer-Verlag, 1997; also “The self-organizing maps,” Proc. IEEE, vol. 78, no. 9, pp. 1464–1480, 1990.
[9]J. C. Bezdek, E. C.-K. Tsao, and N. R. Pal, “Fuzzy Kohonen clustering networks,” Fuzzy Systems, 1992, IEEE International Conference, pp.1035–1043, 1992
[10]T. M. Martinetz, S. G. Berkovich, and K. J. Schulten, “Neural gas network for vector quantization and its application to time-series prediction,” IEEE Trans. Neural Networks, pp. 558–569, 1993.
[11]A. K. Qin and P. N. Suganthan, “Enhanced neural gas network for prototype-based clustering” Pattern Recognition, pp. 1275–1288, 2005.
[12]F. Ancona, S. Ridella, S. Rovetta, and R. Zunino, “On the importance of sorting in neural gas training of vector quantizers,” Proceedings of the ICNN-97, pp. 1804 – 1808, 1997.
[13]C. S. T. Choy and W. C. Siu, “Fast sequential implementation of neural gas network for vector quantization,” IEEE Trans. Commun., pp. 301–304, 1998.
[14]A. S. Atukorale and P. N. Suganthan, “Hierarchical overlapped neural-gas network with application to pattern classification,” Neurocomputing, pp. 165–176, 2000.
[15]T. Martinetz and K. Schulten, “A neural-gas network learns topologies,” Artificial Neural Networks, North-Holland, Amsterdam, pp. 397–402, 1991.
[16]J. L. Deng, “Introduction to grey system theory,” J. Grey Syst., pp. 1–24, 1989.
[17]K. C. Chang and M. F. Yeh, “Grey relational analysis based approach for data clustering,” IEE Proc. Vis. Image Signal Process., vol. 152, No. 2, 2005.
[18]Y. C. Hu, R. S. Chen, Y. T. Hsu, and G. H. Tzebg, “Grey self-organizing feature maps,” Neurocomputing, pp. 863–877, 2002.
[19]W. S. McCulloch and D. D. Barenne, “A logical calculus of the ideas immanent in nervous activity,” Bulletin of Mathematical Biophysics, 1943.
[20]W. S. McCulloch and W. Pitts, “How we know universals: The Perception of Auditory and Visual Forms,” Bulletin of Mathematical Biophysics, 1947.
[21]T. Kohonen, Self-organization and associative memory, 3rd. ed., Berlin, Germany: Springer-Verlag, 1989.
[22]J. A. Kangas, T. Kohonen, and J. T. Laaksonen, “Variants of self-organizing maps,” IEEE Trans. Neural Networks, vol. 1, pp. 93–99, 1990.
[23]A. Rauber, D. Merkl, and M. Dittenbach, “The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data,” IEEE Trans. Neural Networks, vol. 13, No. 6, 2002.
[24]M. Cottrell and J. C. Fort, “A stochastic model of retinotopy: A self – organizing process,” Biological Cybernetics, vol. 53, pp. 405–411, 1986.
[25]D. G. Roussinov and H. Chen, “Information navigation on the web by clustering and summarizing query results,” Inform. Processing Manage., vol. 37, pp. 789–816, 2001.
[26]T. Kohonen, E. Oja, O. Simula, A. Visa, and T. Kangas, “Engineering application of the self-organizing map,” Proceeding of The IEEE, vol. 84, No. 10, pp. 1358–1383, 1996.
[27]T. M. Martinetz, H. J. Ritter, and K. J. Schulten, “Three-dimensional neural net for learning visuomotor coordination of a robot arm,” IEEE Trans. on Neural Networks, vol. 1, pp. 131–136, 1990.
[28]B. Fritzke, “Some competitive learning methods (draft)”, Technical Report, Institute for Neural Computation, Ruhr University, Bochum, 1997.
[29]S. Haykin, Neural networks: a comprehensive foundation, 2nd. ed., Prentice-Hall, Englewood Cliffs, New Jersey, 1998.
[30]http://www.ics.uci.edu/~mlearn/MLRepository.html
[31]P. N. Suganthan, “Hierarchical overlapped SOMs for pattern classification,” IEEE Trans. Neural Networks, vol. 10, No. 1, pp. 193–196, 1999.
[32]D. DeSieno, “Adding a conscience to competitive learning Neural Networks,” IEEE International Conference on, vol. 1, pp. 117–124, 1988.