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研究生:鄒依霖
論文名稱:於資料庫中應用模糊類神經網路挖掘知識_以人力資源管理為例
論文名稱(外文):Applying Fuzzy Neural Network to Discover Knowledge in Database _Using Human Resource Management as an Example
指導教授:黃木榮黃木榮引用關係
學位類別:碩士
校院名稱:國立彰化師範大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:62
中文關鍵詞:知識推論模糊類神經網路類神經網路關聯式規則
外文關鍵詞:Knowledge InferenceFuzzy neural networkNeural NetworkAssociative Rules
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有許多先進的資訊科技不停的被學者們研究出來,然而在企業管理方面的應用則需要更多的努力。本研究希望提出一個模式,讓我們能更方便找出藏在資料中的知識,以提供給管理者在各項企業管理上適當的應用,以做出合適的策略決策;此種特性在專家知識無法取得時,或是規則不易明確得到時,顯得格外重要。
本研究中將提出一個產生及推論知識模式(Producing and Inferring Knowledge Model;P&I Knowledge Model),此模式先以Apriori-like的演算法導出模糊規則,再用模糊類神經網路的方法,訓練規則知識庫,以提供管理者在判斷時,可以透過推論引擎在規則知識庫中的推論引出藏在資料庫中的知識。
本研究以雛型法(Prototyping)為研究方法,利用模糊類神經網路這項成熟的技術,在商業管理領域中提供出另一種方法來產生及推論知識。

There are many advanced information technologies continually developed by the researchers. However, people need more efforts on the applications of business management. This paper proposes a model producing and inferring knowledge to discover knowledge hidden in databases for inferring proper knowledge to make right strategic decision on business management. It is much more important when we cannot get expertise knowledge easily from the experts or when the useless rules cannot be easily held.
The Producing and Inferring Knowledge Model derives fuzzy rules with an Apriori-like algorithm, then introduces fuzzy neural network to train the rule base. When managers make decisions, they can induce the hidden knowledge through the inference engine.
This study will adopt prototyping method to verify the model. We are trying to use fuzzy neural network, a mature technology, with proper rules to produce and infer knowledge.

目錄
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機 1
第三節 研究目的 2
第四節 研究範圍與限制 4
第五節 研究流程 5
第二章 文獻探討 6
第一節 機器學習各種方法 6
第二節 本研究應用之基本概念 7
第三節 類神經網路與模糊邏輯的比較 16
第四節 模糊類神經網路 17
第五節 本章小結 22
第三章 產生與推論知識模式 23
第一節 符號定義 23
第二節 產生與推論知識模式 25
第三節 推論階段 35
第四節 本章小結 39
第四章 產生與推論知識模式雛型_人力資源管理應用 40
第一節 人力資源的重要 40
第二節 系統需求 41
第三節 系統架構 41
第四節 產生階段 42
第五節 推論階段 52
第六節 本章小結 56
第五章 結論與未來工作 57
第一節 研究貢獻 57
第二節 結論 58
第三節 未來研究 58
參考文獻 60

參考文獻
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