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研究生:恩克瑪
研究生(外文):ENKHMAA BATSUKH
論文名稱:使用模糊推理系統解決賽馬的相似性識別 (以香港賽馬為例)
論文名稱(外文):Using Fuzzy Inference System to Solve Similarity Recognition for Racing Horses(Hong Kong horse race as an example)
指導教授:張百畝張百畝引用關係程守雄程守雄引用關係
指導教授(外文):BAE-MUU CHANGSHOU-HSIUNG CHENG
口試委員:蔡鴻旭溫坤禮張百畝
口試委員(外文):HONG-XU CAIKUN-LI WENBAE-MUU CHANG
口試日期:2020-07-10
學位類別:碩士
校院名稱:建國科技大學
系所名稱:服務與科技管理研究所
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:65
中文關鍵詞:賽馬Matlab相似度識別模糊推理系統歸屬函數
外文關鍵詞:Racing horsesMatlabSimilarity recognitionFuzzy inference systemMembership functions
IG URL:ehhhma0320
Facebook:B. Enhmaaa
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本方法設計了一種新穎的賽馬識別方法,它使用模糊推理系統解決賽馬的相似度識別 (FISSRRH)。這個FISSRRH系統利用Matlab軟體中的模糊推理系統 (FIS) 來實現賽馬的相似度識別,以滿足騎師的要求。首先,每匹賽馬都具有五個特徵:年齡、步距、重量、身高和價格。 隨後,這五個特徵被用於建立FIS系統,FIS可以在資料庫D中找出查詢賽馬和每匹樣本賽馬之間的相似度。最後,實驗結果顯示,FISSRRH方法可以達到令人滿意的效果。
A novel approach for racing horses’ recognition, called “Using Fuzzy Inference System to solve Similarity Recognition for Racing Horses (FISSRRH)”, is designed in this paper. The FISSRRH skill utilizes a Fuzzy Inference System (FIS) in Matlab software to achieve similarity recognition for racing horses to meet jockeys’ requirements. First, each racing horse is picked up five features: age, step distance, weight, height, and price, respectively. Subsequently, these five features are employed to build the FIS, which can look for the similarities between a query racing horse and each sampling racing horse in the database D. Finally, experimental results demonstrate that the FISSRRH approach can achieve a satisfying performance in similarity recognition for racing horses under considerations here.
Table of Contents
Acknowledgments..................................................I
摘要.............................................................II
Abstract........................................................III
Table of Contents................................................IV
List of Tables...................................................VI
List of Figures.................................................VII
Chapter 1 Introduction...........................................1
1.1 Objective and motivation for this thesis.....................7
1.2 Organization for this thesis.................................8
Chapter 2 The background.........................................9
2.1 Fuzzy logic..................................................9
2.2 Fuzzy set....................................................9
2.3 Membership function.........................................12
2.4 Fuzzy sets and fuzzy rules in logical operation.............16
2.4.1 Single variable and single rule...........................20
2.4.2 Multiple variables and multiple rules.....................22
2.5 Fuzzy inference system......................................26
Chapter 3 The proposed FISSRRH method...........................30
3.1 Features of extraction for each racing horse............... 32
3.2 The FIS’s construction......................................32
3.3 Training and testing sets’ construction.....................36
Chapter 4 The experimental results..............................38
4.1 The racing horse database D.................................38
4.2 The fuzzified input and output variables....................42
4.3 Fuzzy rules.................................................45
4.5 Defuzification..............................................49
4.6 The recognition performance’s quantitative index............50
4.7 Experimental results........................................52
Chapter 5 Conclusion and future work............................54
References......................................................55


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