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研究生:莊惠棋
研究生(外文):Hui-ChiChuang
論文名稱:模糊決策法於因果關係塑模之研究
論文名稱(外文):A Fuzzy Decision-Making Approach to Cause-Effect Modeling
指導教授:李昇暾李昇暾引用關係
指導教授(外文):Sheng-Tun Li
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊管理研究所
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:2013
畢業學年度:101
語文別:英文
論文頁數:51
中文關鍵詞:因果關係模糊認知圖Hebbian學習法模糊傳遞包絡
外文關鍵詞:Cause-effect relationshipsFuzzy cognitive mapsHebbian learningFuzzy transitive closure
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  • 點閱點閱:163
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要在複雜的系統中描述因果關係並不容易,尤其系統往往是動態的,會隨著許多因素而改變。因此,在組織中如何讓其運作達到最佳且平衡的效能是十分關鍵的。現今,有許多工具已經被廣泛用來描述和探討系統中的因果關係,且已經有效的應用在各個領域,以解決並說明系統動態變化的情形。
本研究使用模糊認知圖來表示一個複雜系統中的因果關係,由於傳統的模糊認知圖存在著一些限制,例如:當系統變動時僅採用最初的固定權重進行運算以及只檢視系統中每個節點間的直接關係,因此,本研究使用Hebbing學習演算法、模糊傳遞包絡和收斂定理來克服這些問題。實驗結果顯示,使用以上這些方法調整權重矩陣後,能讓系統得到更好的效能。此外,我們將此模型應用在醫療資料上,並使用模糊認知圖預測病人得到中風的可能性,提供醫生或病人額外的資訊了解其身體健康狀況。
It is not a simple task to depict cause and effect relations in a complex systems, especially they are always dynamic and constantly changing. However, this task is very critical to achieve optimum and balanced status in any situation. Nowadays, there are a lot of tools for the expression and analysis of the relationship of causes and effects in these systems. These methods or tools have been developed to assist this, and they have been used widely and effectively in various fields.
This study utilizes fuzzy cognitive maps (FCMs) to represent the cause and effect relationships in a complex system. Since fuzzy cognitive maps have some limitations, such as using fixed weights when the system changes, examining direct influences only, and so on, we use Hebbian learning, fuzzy transitive closure and a convergence method to overcome these. We get much better performance from adjusting the weight matrix using this approach. Furthermore, we illustrate the use of this method with real medical data, and predict the probability of getting a stroke using fuzzy cognitive maps. It is anticipated that this can provide extra information to doctors or patients with regard to the health status of the latter.
摘 要 I
Abstract II
誌 謝 III
Table of Contents IV
List of Tables VI
List of Figures VII
Chapter 1 Introduction 1
1.1 Background and research motivations 1
1.2 Research objectives 3
1.3 The process of the research 4
Chapter 2 Literature review 5
2.1 Fuzzy theory 5
2.1.1 Fuzzy set and fuzzy set theory 5
2.1.2 Fuzzy relation 6
2.2 Fuzzy cognitive map 7
2.2.1 Causal network methodology 7
2.2.2 Fuzzy cognitive map 8
2.2.3 Applications of fuzzy cognitive map 13
2.3 Learning methods 16
2.3.1 Unsupervised learning methods 18
2.3.2 Learning methods for FCMs 19
2.3.3 The active hebbian learning algotithm 20
Chapter 3 Research method 24
3.1 Constructing fuzzy cognitive maps 25
3.1.1 Formalization of fuzzy cognitive maps 25
3.1.2 Construction process 27
3.2 Hebbian learning 29
3.2.1 Learning goal 29
3.2.2 Learning algorithm and process 29
3.3 Fuzzy transitive closure 31
3.4 Convergence Method 32
Chapter 4 Experiment and Analysis 33
4.1 Stabilize the weight matrix for FCM 33
4.2 Fuzzy cognitive map model for stroke 37
Chapter 5 Conclusion and future work 43
5.1 Conclusion 43
5.2 Future work 44
References 45
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