臺灣博碩士論文加值系統

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

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