隨著資訊科技的快速發展，越來越多的資訊以文字文件的型態出現在網路上。為了協助使用者找到其所需的文件，資訊擷取系統所扮演的角色也就越來越重要。一個良好的資訊擷取系統可以快速且準確的幫助使用者找到其所需之文件，並依其相關程度加以排序。近年來，有一些學者專家使用平均運算子來處理模糊資訊擷取中的 “AND” 與 “OR” 運算。在本論文中，首先， 我們提出一個新的平均運算子，稱為WPMA運算子，以處理模糊資訊擷取問題。我們並利用一些範例來說明本論文所提之WPMA運算子能解決目前的平均運算子所遭遇到的問題，且證明WPMA運算子所具有的特性。 然後，我們提出一個優先資訊融合方法以作模糊資訊擷取，並提出一個新的方法來處理廣義模糊數的排序。本論文所提的方法能提升資訊擷取系統作文件擷取的效能。

With the rapid development of information technology, more and more information appears in the network in the form of text documents. In order to help users to get their needed documents, the role of information retrieval systems is more and more important. With the help of an information retrieval system, users can get relevant documents ranking by their relevant degrees. In recent years, some researchers have used averaging operators to deal with the “AND” and “OR” operations of users’ fuzzy queries for fuzzy information retrieval. In this thesis, we present new averaging operators, called weighted power-mean averaging (WPMA) operators, to deal with fuzzy information retrieval. We also use some examples to show the proposed WPMA operators can overcome the drawback of the existing averaging operators and prove the properties of the proposed WPMA operators. Then, we present a prioritized information fusion algorithm for handling fuzzy information retrieval problems. We also present a new method for ranking generalized fuzzy numbers. The proposed methods can improve the performance of information retrieval systems for document retrieval.

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