建築皮層為界定人和自然環境的介面。為了因應自然環境變化與人需求改變，因此建築皮層應該要可以調整。在建築物、建築開口、居住環境與居住者都不一定相同的情形下，建築皮層需具有學習能力。所以本研究目的為提出一個可行的架構來建立智慧建築皮層，此架構能提供學習與自動控制建築皮層的能力，使建築皮層能自動調適環境的變化，滿足人對環境舒適的要求。研究方法以類神經網路建構一套系統用來自動控制建築物的外、中、內皮層。在經過虛擬資料庫實驗後，發現隨著訓練資料量的增加，系統輸出控制的平均正確率不斷進步，顯示本系統可有效的學習。另外隨著訓練資料量的增加，預測輸出值與目標輸出值距離的標準差也會逐漸下降，顯示經過不斷的學習，系統的輸出控制也會越來越穩定。

Building skin defines the relation of people and natural environment. It should be adjustable to the changes of people needs and natural environment. Building skins need the ability of learning for the adaptation to different situations under variations of spatial functions, opening, surroundings and occupants of the building. This research advances a feasible framework which can realize smart building skins by providing the ability of learning and automatic controlling to satisfy the demands of people for a comfortable environment by adjusting parameters of the smart building skin. A neural network is used as the control system. We use a set of virtual data to evaluate the system. The result shows that the average accuracy of the system output control increases when the volume of training data increases and shows the system can learn effectively. Furthermore, the standard deviation of the distance between forecast output and target output decreases gradually when the volume of training data increases, and it shows that the output control of the system becomes more stable by continuously learning.

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