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 在冷房中室內風速與溫度是影響人舒適度的主要因素，而風速與溫度可藉由改變風扇和壓縮機的轉速來調整。利用資料分類方法建立出真實系統的模糊模式，可以用來描述冷房中這些變數間的關係。在本論文中先利用KM作快速但粗略的分類，再利用FCM作模糊分類，並且使用我們提出的法則自動調整clusters的數目以增加模式正確性。最後利用Feed-forward Control來展現所建立的模式的可用性。
 In air-conditioned room, the main factors which affect the human comfort are air-velocity and temperature which can be changed by controlling the rotational speed of fan and compressor. To describe the relation among these state variables all over the air-conditioned room, the fuzzy models of real system are built by using data clustering algorithms. It is suggested that a fast and rough clustering is performed by K-means algorithm [1], and then followed by fuzzy clustering with Fuzzy c-means algorithm [2-5]. Also, the number of clusters is automatically adjusted according to certain criteria we proposed to increase model accuracy. Finally, a feed-forward control of the air-conditioner is used to demonstrate the feasibility of the models built.
 Chapter1 Introduction Chapter2 Fuzzy Modeling of Air-conditioned Room 2.1 TS model 2.2 KM Algorithm 2.3 FCM Algorithm 2.4 Clusters Splitting and merging 2.5 Successive Clustering Algorithm 2.6 Training Rule consequents by Least squuare 2.7 Cluster Validity─Scattering Criteria Chapter 3 Design for Thermal Comfort Control 3.1 Thermal Comfort Control 3.2 PID controller 3.3 Feed-forward control Chapter 4 Experimental System 4.1. The Measurement System 4.2. The Control System Chapter 5 Experiment Results and Discussion 5.1 Successive Clustering Algorithm 5.2 Fuzzy model 5.3 Thermal Comfort Control Chapter 6 Conclusion References
 [1]Hui Yuan, Khorram, S., Dai, X.L.,” Applications of simulated annealing minimization technique to unsupervised classification of remotely sensed data”, IEEE 1999 International Geoscience and Remote Sensing Symposium, Volume: 1, 1999 Page(s): 134 —136.[2]Passion, Keven M., Yurkovich, Stephen., FUZZY CONTROL, ADDISON-WESLEY, Inc., U.S.A., 1998.[3]Wang, Li-Xin., A course in fuzzy systems and control, Prentice-Hall, Inc., U.S.A., 1997.[4]Chen, Jin-Liang., Wang, Jung-Hua.,”A new robust clustering algorithm-density-weighted fuzzy c-means”, IEEE International Conference on Systems, Man, and Cybernetics, Volume: 3 , 1999Page(s): 90 —94.[5]Kersten, P.R.,” Fuzzy order statistics and their application to fuzzy clustering”, IEEE Transactions on Fuzzy Systems, Volume: 7 Issue: 6, Dec. 1999 Page(s): 708 —712.[6]Asada, Haruhiko, “User-Adaptable Comfort Control for HVAC Systems”, Transactions of the ASME, September 1994, Vol. 116, Page(s):474~486.[7]Ling, K. V., Dexter, A. L., “Expert Control of Air-conditioning Plant”, Automatica, 1994,Vol. 30, No. 5,Page(s): 761-773.[8]Sousa, J.M., Babuska, R., Verbruggen, H.B., “Internal Model Control with a Fuzzy Model: Application to an Air-Conditioning System”, IEEE International Conference, Volume: 1, 1997Page(s): 207 -212.[9]Euntai Kim, Minkee Park, Seunghwan Ji, Mignon Par,” A new approach to fuzzy modeling”, IEEE Transactions on Fuzzy Systems, Volume: 5 Issue: 3, Aug. 1997 Page(s): 328 —337.[10]Karhunen, J., Malaroiu, S.,” Locally linear independent component analysis”, International Joint Conference on Neural Networks, Volume: 2, 1999 Page(s): 882 —887.[11]Singh, M., Patel, P., Khosla, D., Kim, T.,” Segmentation of functional MRI by K-means clustering”, IEEE Transactions on Nuclear Science, Volume: 43 Issue: 3 Part: 2, June 1996Page(s): 2030 —2036.[12]Gil, M., Sarabia, E.G., Llata, J.R., Oria, J.P., “Fuzzy C-Means Clustering For Noise Reduction, Enhancement And Reconstruction Of 3-D Ultrasonic Images”, IEEE International Conference on, Volume: 1, 1999 Page(s): 465 -472.[13]Thitimajshima, P.,” A new modified fuzzy c-means algorithm for multispectral satellite images segmentation”, IEEE 2000 International Geoscience and Remote Sensing Symposium, Volume: 4, 2000 Page(s): 1684 —1686.[14]Emami, Mohammad R., Türksen, I. Burhan, “Development of A Systematic Methodology of Fuzzy Logic Modeling”, IEEE Transactions On Fuzzy Systems, Volume: 6 Issue: 3, Aug. 1998Page(s): 346 -361.[15]Manish Sarker, B. Yegnanaryana, “A Clustering Algorithm Using Evolutionary Programming”, IEEE International Conference on , Volume: 2 , 1996 Page(s): 1162 —1167.[16]ISO, Moderate thermal environments-determination of the PMV and PPD indices and specification of the conditions for thermal comfort, ISO Standards 7730, New York, International Standards Organization, 1984.[17]Setnes, M., van Drempt, O.J.H.,” Fuzzy modeling in stock-market analysis”, (CIFEr) Proceedings of the IEEE/IAFE 1999 Conference on Computational Intelligence for Financial Engineering, 1999 Page(s): 250 —258.[18]Roubos, J.A., Babuska, R., Bruijn, P.M., Verbruggen, H.B.,” Predictive control by local linearization of a Takagi-Sugeno fuzzy model”, IEEE International Conference on Fuzzy Systems Proceedings, Volume: 1, 1998 Page(s): 37 —42.[19]Fanger, P. O., Thermal Comfort, McGraw-Hill Book Company, 1972.[20]Chang, Shih.Hua., “Fuzzy Modeling and Internal Model Control of Air-Condition Rooms”, Master’s Thesis of Mechanical Engineering, Tatung Institute of Technology, June 1999.[21]Benjamin C. Kuo, Digital Control Systems, Saunders College Pubishing.,U.S.A,1992.[22]Hessburg, Thomas M., Krantz, Donald G., “Feedforward Control (Based on Model Inversion) and System Performance Prediction Using High-Fidelity Nonlinear Dynamic Hydraulic System Modeling” IEEE International Conference on Control Applications, Hartford, CT, October 5-7, 1997.[23]Tae-Gyoo Lee, Jin-Hwan Kim, Ho-Joon Park, Jae-Chul Oh, Uk-Youl Huh,” A speed control of motor systems with a feedforward neural network-its application to SR motor”, Proceedings of the 1996 IEEE IECON 22nd International Conference on Industrial Electronics, Control, and Instrumentation, Volume: 2, 1996Page(s): 898 —903.
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 1 無段風速冷房舒適度控制 2 用群組分合演算法作冷房模糊建模及控制 3 控制用冷房模式之建立

 1 吳惠林（1988），「專上人力勞動報酬的決定因素──台灣的實證分析」，經濟論文叢刊，16;3，357-369。

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