以類免疫系統為基礎之線上學習類神經模糊系統及其應用

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：28

、訪客IP：18.118.119.129

姓名

楊元韶(Yuan-Shao Yang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

以類免疫系統為基礎之線上學習類神經模糊系統及其應用
(An Artificial Immune System based On-line Learning Neuro-Fuzzy System and Its Applications)

相關論文

★ 以Q-學習法為基礎之群體智慧演算法及其應用	★ 發展遲緩兒童之復健系統研製
★ 從認知風格角度比較教師評量與同儕互評之差異：從英語寫作到遊戲製作	★ 基於檢驗數值的糖尿病腎病變預測模型
★ 模糊類神經網路為架構之遙測影像分類器設計	★ 複合式群聚演算法
★ 身心障礙者輔具之研製	★ 指紋分類器之研究
★ 背光影像補償及色彩減量之研究	★ 類神經網路於營利事業所得稅選案之應用
★ 一個新的線上學習系統及其於稅務選案上之應用	★ 人眼追蹤系統及其於人機介面之應用
★ 結合群體智慧與自我組織映射圖的資料視覺化研究	★ 追瞳系統之研發於身障者之人機介面應用
★ 基因演算法於語音聲紋解攪拌之應用	★ 虹膜辨識系統之研究與實作

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

在許多應用中，系統需要在不會破壞舊有的資訊的前提下，能夠快速地學習新的資訊和微調舊有的資訊，這就是所謂的線上學習的特性。對於一個有效的辨識系統來說，能具備線上學習的特性是相當吸引人的。
人體免疫系統是十分地複雜的，它的許多特性與機制吸引了許多的研究者注意，近幾年來，有很多類免疫系統(AIS)的產生，這些不同的類免疫系統採用了在人體免疫系統裡一些不同的機制，來解決所要處理的問題。
在本論文中，我們提出了一個新的線上學習的類神經模糊系統，採用了人體免疫系統中的某一些特性，我們稱此類神經模糊系統為“以類免疫系統為基礎的類神經模糊系統” 。此系統在學習的過程中，能夠以漸進式的方式來建構系統，並可以應用在圖形識別與函數逼近的問題上。除了用數個人造資料集，並且也以一些真實的資料集來測試其效能，尤其特別的是，我們也將此系統應用於背光影像的補償處理。

摘要(英)

In some applications, systems should be able to learn new classes and refine existing classes quickly and without destroying old class information. This property is referred to as on-line learning and it is a very appealing property for an efficient pattern recognition system.
The immune system is a highly complicated system. Many properties of immune systems attract a great amount of attentions from compute scientists and engineers. In recently years, many artificial immune systems have been proposed. Different artificial immunes systems are inspired by different subsets of the available metaphors.
In this paper, we present an on-line learning neuro-fuzzy system which was inspired by part of the mechanisms in immune systems. We name the proposed neuro-fuzzy system as the artificial immune system based neuro-fuzzy system (AISNFS). During the learning procedure, a neuro-fuzzy system can be incrementally constructed. AISNFS can be applied in pattern recognition and function approximation problems. The performance of the propose AISNFS is evaluated by not only some artificial data sets but also some real data sets. Especially, we apply the proposed AISNFS in the compensation of backlight images.

關鍵字(中)

★ 線上學習
★ 類神經網路
★ 模糊系統
★ 類免疫系統

關鍵字(英)

★ artificial immune systems
★ on-line learning
★ neural networks
★ fuzzy systems

論文目次

摘要……………………………………………………………………………I
Abstract………………………………………………………………………III
誌謝……………………………………………………………………………V
目錄……………………………………………………………………………VI
圖目錄…………………………………………………………………………IX
表目錄…………………………………………………………………………XI
第一章緒論……………………………………………………1
1.1 研究動機………………………………………………1
1.2 論文架構………………………………………………2
第二章線上學習系統探討……………………………………3
2.1 模糊適應共振理論映射圖……………………………………3
2.1.1 網路架構………………………………………………3
2.2.2 模糊適應共振理論演算法……………………………4
2.2 簡化模糊適應共振理論映射圖………………………7
2.2.1 網路架構………………………………………………7
2.2.2 學習演算法……………………………………………8
2.3 模糊最大最小類神經網路……………………………10
2.3.1 網路架構………………………………………………10
2.3.2 學習演算法……………………………………………11
2.4 改良式簡化模糊適應共振理論映射圖………………12
2.4.1 網路架構………………………………………………12
2.4.2 學習演算法……………………………………………13
2.5 線上系統的特性分析…………………………………15
第三章以類免疫系統為基礎的類神經模糊系統……………18
3.1 免疫系統介紹…………………………………………18
3.2 AISNFS與免疫系統……………………………………21
3.3 AISNFS之圖形辨別……………………………………23
3.3.1 AISNFS對於圖形識別之網路架構……………………23
3.3.2 AISNFS對於圖形識別之學習演算法…………………23
3.3.3 網路測試………………………………………………27
3.4 AISNFS之函數逼近……………………………………28
3.4.1 函數逼近之網路架構…………………………………28
3.4.2 AISNFS對於函數逼近的學習演算法…………………29
3.4.3 網路測試………………………………………………33
3.5 實驗模擬………………………………………………34
3.5.1 二維資料之579……………………………………… 34
3.5.2 雙螺旋資料……………………………………………36
3.5.3 語者辨識資料集………………………………………39
3.5.4 鳶花尾資料集(Iris)…………………………………41
3.5.5 UCI 資料庫……………………………………………42
3.5.5.1 教學評估資料集………………………… 43
3.5.5.2 大地衛星資料…………………………… 44
3.5.5.3 避孕方法的選擇………………………… 45
3.5.5.4 肝癌測試………………………………… 46
3.5.5.5 乳癌測試………………………………… 47
3.5.6 函數逼近測試…………………………………………48
第四章背光影像補償…………………………………………51
4.1 背光影像研究探討……………………… 51
4.2 背光影像自動補償演算法……………… 54
4.3 實驗結果………………………………… 61
第五章結論與建議……………………………………………73
參考文獻………………………………………………………………………74

參考文獻

[1] C. A. Janeway等原著 ; 楊志元等編譯，免疫生物學，藝軒圖書發行，民國九十一年。
[2] 汪蕙蘭，護用微生物免疫學，五南圖書出版公司印行。
[3] 呂維哲，模路類神經網路為架構之遙測影像分類器設計，國立中央大學資訊工程研究所碩士論文，民國九十一年。
[4] 郭家豪，背光影像補償及色彩減量之研究國立中央大學資訊工程研究所碩士論文，民國九十二年。
[5] S. A. and M. S. Lan, “A method for fuzzy rules extraction directly from numerical data and its application to pattern classification,” IEEE Trans. on Fuzzy Systems, vol. 3, no.1, pp. 18–28, 1995.
[6] C. L. Blake and C. J. Merz, “UCI repository of machine learning databases,”http://www.ics.uci.edu/~mlearn/MLRepository.html, 1998.
[7] J. C. Bezdek, Fuzzy mathematics in pattern classification, Ph.D Thesis, Cornell University, 1973.
[8] L. N. de Castro and F. J. Von Zuben, “Artificial immune systems: part I – basic theory and applications”, Technical Report – RT DCA 01/99, pp. 1-95, 1999.
[9] L. N. de Castro and F. J. Von Zuben, “Artificial immune systems: part II – a survey of applications”, Technical Report – RT DCA 02/00, pp. 1-65, 2000.
[10] G. A. Carpenter, S. Grossberg, N. Markuzon, J.H. Reynolds, and D.B. Rosen, “Fuzzy ARTMAP: a neural network architecture for incremental supervised learning of analog multidimensional maps,” IEEE Trans. on Neural Networks, vol. 3, pp. 698-713, 1992.
[11] J. Caeter, “The immune system as a model for pattern recognition and classification,” Journal of American Medical Informatics Association, vol. 7, no. 1, pp. 28-41, 2000.
[12] Y. Deng, B. S. Manjunath and Hyundoo Shin. Color image segmentation. IEEE Computer Society Conference on Computer Vision and Pattern, 2:446-451,1999
[13] D. Dasgupta, “Artificial neural networks and artificial immune systems: similarities and differences”, IEEE International Conference on Systems, Man, and Cybernetics, pp. 873-878, 1997.
[14] D. Dasgupta, “Immunity-based intrusion detection system: a general framework”, Proc .of the 22nd NISSC, 1999.
[15] D. Dasgupta, Artificial immune systems and their applications, Springer-Verlag, 1998.
[16] D. Dasgupta and S. Forrest, “Novelty detection in time series data using ideas from immunology,” The 5th International Conference on Intelligent Systems, pp. 82-87, 1996.
[17] J. E. Hunt and D. E. Cooke, “Learning using an artificial immune system”, Journal of Network and Computer Applications, vol. 19, pp. 189-212, 1996.
[18] R. A. Finan, A. T. Sapeluk, and R. I. Damper, “Comparison of multilayer and radial basis function neural networks for text-dependent speaker recognition” IEEE International Conference on Neural Networks, pp. 1992-1997, 1996
[19] B. Gabrys and A. Bargiela, " General fuzzy min-max neural network for clustering and classification," IEEE Trans. on Neural Networks, vol. 11, pp. 769-783, 2000.
[20] R. C. Gonazlez and R. E. woods, Digital image processing, 2nd. Addison-wesley, 1992.
[21] S. A. Hofmeyr and S. Forrest, “Architecture for an artificial immune system,” Evol. Comp., vol. 8, no. 4, pp. 443-473, 2000.
[22] S. Halgamuge and M. Glesner, “Neural networks in designing fuzzy systems for real world applications,” Fuzzy Sets and Systems, vol. 65, pp. 1-12, 1994.
[23] T. Haruki and K. Kikuchi, “Video camera system using fuzzy logic,” IEEE Transactions on Consumer Electronics,Vol.38, No.3 , pp.624-634, Aug. 1992.
[24] R. Hummel, “Image enhancement by histogram transformation,” Comp. Graph. Image Process., vol. 6, pp. 184-195, 1977.
[25] A. K. Jain, “Fundamentals of digital image processing,” Englewood Cliffs, NJ: Prentice-Hall, 1989.
[26] D. J. Ketcham, R. Lowe, and W. Weber, “Seminar on image processing,” in Real-Time Enhancement Techniques, 1976, pp. 1-6.Hughes Aircraft.
[27] Y. T. Kim, “Contrast enhancement using brightness preserving bi-his togram equalization,” IEEE Trans. Consumer Electron., vol. 43 , no. 1, pp. 1-8, Feb. 1997.
[28] T. K. Kim, J. K. Paik, and B. S. Kang, “ Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering,” IEEE Trans. on Consumer Electronics, vol. 44, no. 1, pp. 82-86, Feb. 1998.
[29] J. Y. Kim, L. S. Kim, and S. H. Hwang: “An advance contrast enhancement using partially overlapped sub-block histogram Equalization,” IEEE Tran. on Circuits and Systems for Video Technology, vol. 11, no. 4, pp. 475-484, April 2001.
[30] K. KrishnaKumar and J. Neidhoefer, “Immunized adaptive critics”, IEEE International Conference on Neural Networks, pp. 2283-2287, 1997.
[31] H. M. Kim and J. M. Mendel, “Fuzzy basis functions: comparisons with other basis functions,” IEEE Trans. on Fuzzy Systems, vol. 3, no. 2, pp. 158-168, 1995.
[32] N. Kasabov and B. Woodford, “Rule insertion and rule extraction from evolving fuzz neural networks: Algorithm and applications for building adaptive, intelligent expert systems,” Proc. of IEEE Int. Conf. Fuzzy Systems 9, vol. 3, Seoul, Korea, pp. 1406-1411, 1999.
[33] T. Kasuba, “Simplified Fuzzy Adaptive Resonance Theory Map”, AI Expert, pp. 18-25, Nov 1993.
[34] Y. W. Lin and S. U. Lee, “On the color image segmentation algorithm based on the thresholding and the fuzzy C-means techniques,” Pattern Recognition, vol. 23, no. 9, pp. 935-952, 1990.
[35] C. W. Le and Y. C. Shin, “Construction of fuzzy basis function networks using adaptive least squares method,” IFSA World Congress and 20th NAFIPS Int. Conf., pp. 2630-2635, 2001.
[36] T. S. Lim, W. Y. Loh, and Y. S. Shih, “Comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms,” Machine Learning, vol. 40, pp. 203-229, 2000.
[37] Y. Lin and G. A. Cunningham III, “A new approach to fuzzy-neural system modeling,” IEEE Trans. on Fuzzy Systems, vol. 3, pp. 190–197, 1995.
[38] K. J. Lang, and M. J. Witbrock, “Learning to tell two spirals apart,” in Proc. 1998 Connectionist Models Summer School, pp. 52-59, 1989.
[39] D. F. McCoy and V. Devarajan, “Artificial immune systems and aerial image segmentation,” IEEE International Conference on Systems, Man, and Cybernetics, pp. 867-872, 1997
[40] A. Morimura, K. Uomori, Y. Kitamura, A. Fujioka, J.Harada, S. Iwamura and M.Hirota, “A digital video camera system,” IEEE Transactions on Consumer Electronics, Vol. 36, No.4, pp.866-875, Nov. 1990
[41] M. Murakami and N. Honda, “An exposure control system of video cameras based on fuzzy logic using color information,” Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, Vol. 3, 1996
[42] H. Meshref and H. VanLandingham, “Artificial immune systems: application to autonomous agents” IEEE International Conference on Systems, Man, and Cybernetics, pp. 61-66, 2000.
[43] J. Moody and C. J. Darken, “Fast learning in networks of locally tuned processing units,” Neural Computation, vol. 1, pp. 181-194, 1989.
[44] M. W. Mak, W. G. Allen and G.C. Sexton, “Speaker identification using radial basis functions”, Third International Conference on Artificial Neural Networks, pp. 138-142, 1993.
[45] V. Moonasar and G. K. Venayagamoorthy, “Speaker identification using a combination of different parameters as feature inputs to an artificial neural network classifier,” AFRICON999 IEEE, pp. 189-194, 1999.
[46] D. Nauck and R. Kruse, “A neuro-fuzzy method to learn fuzzy classification rules from data,” Fuzzy Sets Syst., vol. 89, no. 3, pp. 277–288, 1997.
[47] H. Narazaki and A. Ralescu, “An improved synthesis method for multilayered neural networks using qualitative knowledge,” IEEE Trans. on Fuzzy Systems, vol. 1, pp. 125–137, 1993.
[48] M. Russo, “Genetic fuzzy learning,” IEEE Trans. on Evol. Comput., vol. 4, pp. 259–273, Sept. 2000.
[49] M. Russo, “FuGeNeSys—a fuzzy genetic neural system for fuzzy modeling,” IEEE Trans. on Fuzzy Systems, vol. 6, pp. 373–388, 1998.
[50] A. Rizzi, F. M. F. Mascioli, and G. Martinelli, "Generalized min-max classifier," Proc. FUZZ-IEEE 2000, vol. 1, pp. 36-41, San Antonio, TX, May 2000.
[51] S. Shimizu, T. Kondo, T. Kohashi, M. Tsurata, and T. Komuro, “A new algorithm for exposure control based on fuzzy logic for video cameras,” IEEE Transactions on Consumer Electronics, Vol.38, No.3, pp.617-623, Aug. 1992.
[52] J. A. Stark, “Adaptive image contrast enhancement using generalizations of histogram equalization,” IEEE Tran. on Image Processing, vol. 9, no. 5, pp. 889-896, May 2000.
[53] P. K Simpson, “Fuzzy min-max neural networks,” in Proc. 1991 Int. Joint Conf. Neural Networks, pp. 1658-1669, Singapore, Nov. 18-21, 1991.
[54] P. K Simpson, “Fuzzy min-max neural networks─Part 1: Classification,” IEEE Trans. on Neural Networks, vol. 3, no. 5, pp.776-786, Sept. 1992.
[55] P. K Simpson, “Fuzzy min-max neural networks─Part 2: Clustering,” IEEE Trams. Fuzzy System, vol. 1, no. 32-45, Feb. 1993.
[56] V. T. Tom and G. J. Wolfe, “ Adaptive histogram equalization and its applications,” SPIE Applicat. Dig. Image Process., vol. 359, pp. 204-209, 1982.
[57] J. Timmis, M. Neal, and J. Hunt, “An artificial immune system for data analysis”, Biosystems, vol. 55, pp. 143-150, 2000.
[58] S. Paul and S. Kumar, “Subsethood-product fuzzy neural inference system (SuPFuNIS),” IEEE Trans. on Neural Networks, vol. 13, no. 3, pp. 578–599, 2002.
[59] A. Watkins and L. Boggess, “A new classifier based on resource limited artificial immune systems”, Proceedings of the 2002 Congress on Evolutionary Computation, vol. 2, pp. 1546–1551, 2002.
[60] L. X. Wang and J. M. Mendel, “Fuzzy basis functions, universal approximation, and orthogonal least-squares learning”, IEEE Trans. on Neural Networks vol. 3, no. 5, pp. 807-814, 1992.
[61] D. X. Zhong and H. Yan. “Color image segmentation using color space analysis and fuzzy clustering,” IEEE Signal Processing Society Workshop, 2:624-633.

指導教授

蘇木春(Mu-Chun Su)

審核日期

2004-7-6

推文