許多設計師經常利用案例在室內設計的過程中輔助業主與設計師溝通。而在利用案例過程中必須進行案例擷取，即從過往的工程案例中，挑選出最具有參考價值的案例，提供給業主或設計師做為決策參考與溝通媒介。
因此本研究的目的是探討類神經網路對於擷取案例上的可行性，即利用類神經網路Artificial Neural Network辨識與篩選相似的空間格局作為設計師參考的案例。而探討的方式則是利用研究所設計的方法，以類神經網路來學習專家挑選案例的能力，再以專家的相似度判斷作為標準，評估類神經網路的辨識能力。
在研究設計的方法上，首先設計空間格局輸入的方式，再讓專家提出相似度判斷的方式與結果，類神經網路學習專家的相似度判斷和作出相似度預測，最後將專家的相似度與類神經網路的預測做比較，以比較後的準確率證明類神經網路的預測能力優劣判斷。在類神經的學習與預測的過程中，為了配合類神經網路學習的需要設計增加案例的方法，使得類神經的學習具有效率，並得以對未曾學習過的案例進行預測。
在評估類神經網路的篩選能力過程中，將利用比對與分析相似度較高的兩個案例，證明相似度高的案例在溝通設計的過程中有相當程度的參考價值。其比較的方法是比對兩個案例是否可以相互參考對方的空間格局的設計，或者直接轉換成相似案例的空間格局。
藉由上述的案例分析研究將可證明類神經網路可以辨別各空間平面的相似度，而且接近專家相似度判斷的水準。

Many interior designers use cases to aid communication with the clients. The reuse of previous cases requires storing and organizing information of prior cases, and an efficient process to retrieve adequate cases for reference in the communication. Prior cases are useful information for decision making in housing refurbishment. Cases with similar layouts are valuable information resources for non-professional housing owners to derive a realistic image over the quality, the cost and the process of the project. Recognition of housing layout patterns is an important and interesting issue in research.
This thesis describes the approach of using neural network to retrieve cases with similar layouts, which is an important step for retrieving similar cases for the references within design communication. This research proposes a method to encode the spatial layout of a house according to the orientation of functional spaces and the types of indoor-outdoor interfaces. Pairs
of encoded layout are input to a three-layer neural network for training. After the training converges to a satisfactory status, other cases are input to the network for testing. The test shows that the output of the neural network is very close to the output of a similarity function devised by a design expert.
The result confirms that neural network can be used to retrieve similar cases for design communication between interior designers and users. It is expected that with the neural network, a case- based system for supporting communication in a housing refurbishment project can be derived.

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