應用於觀念溝通之觀念模糊集對應設計方法

Abstract

觀念溝通是人機介面中一個很重要的部分，它可以讓使用者與系統之間順暢地交換訊息。本論文引入了觀念模糊集來表示現實世界中抽象與具體的觀念，並提出了幾種對映的機制來建立這些觀念之間的關係。觀念的對映必須是雙向的，由抽象觀念對映至具體的觀念是一種觀念解釋的過程，而由具體觀念對映至抽象的觀念則是一種觀念辨識的過程。首先我們提出以雙向s形函數來達成觀念對應的方法，觀念的雙向對映是以不同的架構來達成，其次我們又引用解模糊關係方程式組來達成對應，其效果則是令人滿意的。最後我們又引用了MLP神經網路，同樣以不同網路的來記憶觀念的雙向對應，在此我們利用BP演算法來學習MLP神經網路的權重矩陣，最後以MLP神經網路來達成觀念溝通，它的效果經過驗證也是一個可行的方法。

Concept communication is an important issue of man machine interface. It provides the smooth communication between man and system. This thesis introduces conceptual fuzzy set (CFS) to represent the abstract concepts and concrete concepts in the real world. Concept mapping must be bidirectional. Mapping from abstract concepts to concrete concepts is considered as concept recognition. Mapping from concrete concepts to abstract concepts is considered as concept interpretation. We propose two mapping schemes to relate these two type concepts. The Bipolar Sigmoid Function is first applied for concept mapping. The forward and backward mappings of concepts are archived by adopting two different structures, respectively. We have also applied the solving of fuzzy relational equations to concept mapping. These two approaches produce very accuracy results. In a functional mapping respective instead, the multilayer perceptron neural network can also be utilized to the concept mapping problem.