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論文名稱(外文):A Fuzzy Decision-Making Approach to Cause-Effect Modeling
指導教授(外文):Sheng-Tun Li
外文關鍵詞:Cause-effect relationshipsFuzzy cognitive mapsHebbian learningFuzzy transitive closure
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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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