臺灣博碩士論文加值系統

本研究發展出結合知識庫、模式庫、資料庫、行動裝置和無線網路的行動知識管理決策支援系統MKMDSS，系統主要架構包含多重代理人、知識推理引擎、知識庫、模式庫和資料庫等元件。透過系統化快速專業知識的蒐集、萃取、分析、儲存，並利用推理引擎和類神經網路預測將推理和預測結果，運用無線網路提供決策者做為下達決策之依據。首先，在規則推理方面，彙整國內企業專業經理人對於企業經營規則與經營績效的看法，轉化為實用規則，並利用多重代理人對MIS/ERP資料庫進行資料採礦，然後採用知識推理引擎以前向鏈結推理的技術進行推理，推理出企業經營上的問題並提供決策建議。其次，利用類神經網路的預測模式，來進行預測未來企業經營的績效，並協助決策者方便了解未來企業的趨勢。最後，行動知識管理決策支援系統之知識庫提供了規則的透通性，使得決策者可以隨時新增、修改及刪除規則，而不會影響系統程式的運作。

This study develops a mobile knowledge management decision support system (MKMDSS). The system consists of a knowledge base, a model base, and a database. By using multi-agent technology, it can search and analyses important financial information from the MIS/ERP database based on data mining technology. then store in the database. Through systematical and quick collection, extraction, and analysis, the database agent stores critical rules into the knowledge base so the knowledge reasoning engine is able to infer alternatives for making decisions. Next, the system uses the forward chaining reasoning technique to carry on reasoning. In addition, we use artificial neural network models to predict some financial data such as total sales, total profits, etc., and know future trend. Finally, MKMDSS supports rule's transparency, when the user maintains any rules, it does not affect the programs so it is easy to use and maintain.

摘　要 i
ABSTRACT ii
誌　謝 iii
目　錄 iv
表目錄 vii
圖目錄 ix
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究架構 4
1.4 研究方法 5
1.5 研究範圍與限制 8
第二章 文獻探討 9
2.1 決策支援系統 9
2.1.1 決策支援系統的應用 9
2.1.2 決策支援系統的理論與技術 11
2.2 行動商務 17
2.2.1 行動商務的應用 17
2.3 知識管理 18
2.4 智慧型代理人 18
2.5 多重代理人 20
2.5.1 多重代理人的應用 21
2.6 類神經網路 22
2.6.1 類神經網路的應用 24
2.6.2 類神經網路的理論與技術 25
第三章 系統建構理論與技術 28
3.1 行動知識管理決策支援系統架構 28
3.2 前向鏈結與後向鏈結推理技術 30
3.2.1 前向鏈結推理 32
3.2.2 後向鏈結推理 34
3.2.3 前向和後向鏈結推理使用時機 36
3.3 知識庫 37
3.3.1 企業經營管理規則 39
3.4 資料庫 43
3.4.1 系統權限管理資料定義 45
3.4.2 企業經營規則管理資料定義 51
3.4.3 推理排程管理資料定義 55
3.4.4 知識推理結果報表定義 60
3.4.5 財務績效指標資料定義 64
3.5 類神經網路應用與模式庫 67
第四章 系統建置與評估測試 70
4.1 系統分析 70
4.2 系統建置 74
4.3 知識推理引擎 75
4.4 自動推理排程工具 78
4.5 類神經網路預測 79
4.5.1 資料正規化 79
4.5.2 類神經網路架構 79
4.5.3 類神經網路訓練與測試 80
4.6 系統功能 83
4.6.1 知識推理 86
4.6.2 知識更新 91
4.6.3 財務績效查詢 94
4.6.4 財務預測 97
4.6.5 系統管理設定 100
第五章 結論與建議 102
參考文獻 103
附錄 109
A 推理引擎程式碼 109

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