ㄧ個可以解釋因果關係的分類系統，必須具備容易解釋語意的表示方式以及使用方便等特性。因此本研究建構於類神經-模糊網路之上，利用其模糊理論之可解釋性與類神經網路之學習能力對樣本進行訓練以期獲得一個優越的模糊分類器。其中對特徵選取以及歸屬函數最佳化均分別用了不同之方法進行試驗，同時提出如何利用K-means演算法獲得歸屬函數初始參數之方法，並提供一特徵選取方式，使所設計之模糊分類器可以用最低特徵考量達至最高推論精確度。
為克服類神經-模糊網路分類器繁瑣之最佳化過程，本研究提出一個垂直合併型歸屬函數概念之快速圖形式模糊分類器，將原本模糊集合之歸屬度由值之水平移動進行歸屬度之計算方式，改由由各值之垂直移動對應模糊區間之計算方式，同樣可以藉由圖形化之概念進行可讀性之解釋，同時步驟簡易且具可解釋性，更重要的是大量減低歸屬函數之使用，且仍可保有相當高之準確率。而本研究所提出之演算法均經由實驗證明其可行性，並進行分析與討論。

An efficient and simple decision support system must have the characteristics such as interpretable, easy understanding, convenient, et al. For this reason, the designed classifier in this study was based on a neuro-fuzzy network to combine the transparent characteristic of fuzzy system and learning ability of neural network. First, this study proposes a refined K-means algorithm and a gradient-based learning algorithm to logically determine and adaptively tuned the fuzzy membership functions for the employed neuro-fuzzy network. Moreover, this study also uses grey relational algorithm to perform feature selection and proposes a novel feature reduction algorithm to overcome the drawbacks of grey relational algorithm.
Because optimized processes contain complex and long steps, this study proposes a Fast Graph Fuzzy Classifier (FGFC) which has a novel determining scheme of the membership function degree and can prevent to confront an abstruse classifier algorithm as well as keep the advantages of the traditional fuzzy systems. All of the above-mentioned methods were implemented and analyzed in this study.

[1] R. Lee and J. Liu, “iJADE WeatherMAN: a weather forecasting system using intelligent multiagent-based fuzzy neuro network,” IEEE Trans. Sys. Man. Cyber., vol. 34, no. 3, pp.369-377, 2004
[2] H. Li and D. Enke, “Forecasting series-based stock price data using direct reinforcement learning,” Proc. 2004 Int. Conf. neural networks, vol. 2, pp.1103-21108, 2004
[3] C. Fredembach, M. Schroder, and S. Susstrunk, “Eigenregions for image classification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 12, pp.1645-1649, 2004
[4] P. R. Innocent and R. I. John, “Computer aided fuzzy medical diagnosis,” Artif. Intell. Med., vol. 162, pp.81-104, 2004.
[5] UCI(University of California, Irvine) Repository of Machine Learning Databases [on line].
Available: http://www.ics.uci.edu/~mlearn/MLSummary.html
[6] C. T. Lin and C. S. G. Lee, “Neural network based fuzzy logic control and decision system,” IEEE Trans. Computers, vol. 40, pp. 1320–1335, 1993.
[7] R. P. Pedro and A. Dourado, “Interpretability and learning in neuro-fuzzy systems,” Fuzzy Sets and Systems, vol. 147, pp. 17-38, 2004
[8] J. Shanahan, B.T. Thomas, M. Mirmehdi, M. Martin, N. Campbell, J. Baldwin, “A soft computing approach to road classification,” J. Intell. Robot. Systems vol.29, pp.349–387. 2000
[9] D. Fuessel, R. Isermann, “Hierarchical motor diagnosis utilizing structural knowledge and a self-learning neuro-fuzzy scheme, ” IEEE Trans. Ind. Electron. vol. 47, pp.1070–1077, 2000.
[10] E. Binaghi, I. Gallo, P. Madella, “A neural model for fuzzy Dempster–Shafer classifiers, ” Internat. J. Approx. Reason, vol.25, pp.89–121, 2000
[11] H.R. Maier, T. Sayed, B.J. Lence, “Forecasting cyanobacterial concentrations using B-spline networks, ” J. Comput. Civil Eng, vol. 14, pp.183–189, 2000.
[12] M.B. Gorzalczany, Z. Piasta, “Neuro-fuzzy approach versus rough-set inspired methodology for intelligent decision support,” Inform, vol.120, pp.45–68, 1999.
[13] A. Stoica, “Learning eye-arm coordination using neural and fuzzy techniques, in: H.-N. Teodorescu,” Soft Computing in Human Related Sciences, pp. 37–66, 1999.
[14] C.-T. Lin, “A neural fuzzy control scheme with structure and parameter learning, ” IEEE Trans. Fuzzy Sets and Systems, vol. 70, pp. 183-212, 1995
[15] S. Osowski and T. H. Linh, “ECG beat recognition using fuzzy hybrid neural network,” IEEE Trans. Biomed. Eng. vol. 48, pp.1265-1271, 2001.
[16] P. Mousavi, R. K. Ward, S. S. Fels, M. Sameti, and P. M. Lansdorp, “Feature analysis and centromere segmentation of human chromosome images using an iterative fuzzy algorithm,” IEEE Trans. Biomed. Eng. vol. 49, pp.363-371, 2002.
[17] D. Nauck and R. Kruse, “Obtaining interpretable fuzzy classification rules from medical data,” Artif. Intell. Med., vol. 16, pp.149-169, 1999.
[18] T. P. Hong and C. Y. Lee, “Induction of fuzzy rules and membership functions from training examples,” Fuzzy Sets and Sys., vol. 84, pp.33-47, 1996.
[19] J. Valente and Oliveira, “Semantic Constraints for Membership Function Optimization network,” IEEE Trans. Sys. Man. Cyber., vol. 29, no. 1, pp.128-138, 1999
[20] T. P. Hong and C. Y. Lee, “Induction of fuzzy rules and membership functions from training examples,” Fuzzy Sets and Sys., vol. 84, pp.33-47, 1996.
[21] J. Valente and Oliveira, “Semantic Constraints for Membership Function Optimization network,” IEEE Trans. Sys. Man. Cyber., vol. 29, no. 1, pp.128-138, 1999
[22] R. Mikut, J. Jakel and L. Groll, “Interpretability issues in data-based learning of fuzzy systems,” Fuzzy Sets and Systems, pp.179–197, 2005
[23] Julong, D.,“Introduction to Grey System Theory”, The Journal of Grey System, 1, pp.1-24, 1989.
[24] J. A. Roubos, M. Setnes, and A. Janos, “Learning fuzzy classification rules from labeled data,” Inform. Sci., vol. 150, pp.77-93, 2003.
[25] H.M. Lee, C.M. Chen, T.M. Chen and Y.L. Jou, “An Efficient Fuzzy Classifier with Feature Selection Based on Fuzzy Entropy,” IEEE Trans. Sys. Man. Cyber., vol. 31, no. 3, pp.426-432, 2001
[26] D. Chakraborty and N.R. Pal, “A neuro-Fuzzy Scheme for Simultaneous Feature Selection and Fuzzy Rule-Based Classification,” IEEE Trans. Neural Network., vol. 15 , pp. 110–123,2004
[27] C. Xiaoguang and H. John “Evolutionary Design of a Fuzzy Classifier From Data,” IEEE Trans. Sys. Man. Cyber., vol. 34, no. 4, pp.1894-1906, 2004.
[28] H. Ishibuchi and T. Yamamoto, “Effects of three-objective genetic rule selection on the generalization ability of fuzzy rule-based systems,” Proc. 2nd Int. Conf. Evolutionary Multi-Criterion Optimization, Faro, Portugal, Apr. 8–11, 2003, pp. 608–622.
[29] J. S. Wang and G. C. S. Lee, “Self-adaptive neuro-fuzzy inference system for classification application,” IEEE Trans. Fuzzy Syst., vol. 10,pp. 790–802, Dec. 2002
[30] Chih-Ming Chen, Hahn-Ming Lee and Ya-Hui Chen, “Personalized E-Learning System Using Item Response Theory,” Computers & Education, vol. 44, no. 3, pp. 237-255, 2005.
[31] Chih-Ming Chen, Chao-Yu Liu and Mei-Hui Chang, “Personalized Curriculum Sequencing Using Modified Item Response Theory for Web-based Instruction,” Expert Systems with Applications, vol. 30, Issue 2, pp. 378-396, 2006.
[32] Baker and Frank B., Item Response Theory: Parameter Estimation Techniques, New York: Marcel Dekker, 1992.