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研究生:宋裕民
研究生(外文):Yu-Min Sung
論文名稱:模糊類神經網路理論於信用之識別
論文名稱(外文):Credic Identification By Using Fuzzy Neural Network Theory
指導教授:林 進 燈林 錫 寬
指導教授(外文):Chin-Teng LinShir-Kuan Lin
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電資學院學程碩士班
學門:工程學門
學類:電資工程學類
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:56
中文關鍵詞:模糊類神經網路信用之識別支持向量機制
外文關鍵詞:Fuzzy Neural NetworkCredic IdentificationSupport Vector Machine
相關次數:
  • 被引用被引用:10
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  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
本篇論文應用一個支持向量機制(Support Vector Machine,SVM)及一個具有自我建構能力之模糊類神經網路(Self-cOnstructing Neural Fuzzy Inference Network,SONFIN),信用卡核卡評估的分析系統。本論文選取申請者的一些基本資料作為網路的輸入,而信用摘要作為網路的輸出,整個模擬核卡評估目標分為兩方向,第一方向以銀行實際發卡情況的角度,即對於申請者的基本資料利用SVM加以分析後進而判斷核卡與否來探討,第二方向利用SONFIN對於申請者之違
約判斷率進行探討研究。
其中SVM 是一種有規則的架構,對於模糊學習法則過程中SVM 學習機制提供一個架構從學習訓練資料集合中了解SVM,並且此學習機制使用核心函數完整的描述模糊理論系統。因此,藉由SVM本身即存在不需要決定法則數量的優勢,整個模糊推論系統可視為一連串模糊基本函數的衍生。然而SONFIN是一個五層架構類神經網路,其中
第一層為一個輸入層;第二層為語言標記層,用來描述如大、小等語言變數;第三層為模糊法則中的命題部運算層;第四層為推論部層;
第五層為輸出層。
最後經由本論文所提方法之判斷結果可作為金融機構對於申請者財務狀況表現的指標參考,進而考慮是否給予申請者受與信用,擔負
風險。
This thesis has been emploied (Support Vector Machine,SVM) and (Self-cOnstructing Neural Fuzzy Inference Network,SONFIN)together as an analytic system of credit card approval evaluation. Some of the customer’s basic information are chosen as the input of the network; then the credit summary was got as the output of the network. There are two goals of the simulation of the credit card approval : one is using SVM from the bank’s point of view, to judge the approval of credit card by analyzing applicants’ information ; the other is using SONFIN from the accuracy of the judgment to
applicants’ annually breach.
The SVM provides an architecture to extract support vectors for generating fuzzy IF-Then rules from the training data set , and a method to describe the fuzzy system in terms of kernel functions. Thus , it has the inherent advantage that the model does not have to determine the number of rules in advance, and the overall fuzzy inference system can be represented as series expansion of fuzzy basis functions. Moreover ,the SONFIN contains five-layers of constructed network. The summary of the five-layer constructed network is as follows:1st layer:input layer;2nd layer:linguistic label layer, such as:large, small, etc;3rd layer:forms the formula of precondition layer of the fuzzy rule ;4th layer: consequent layer ;5th:layer : output layer.
Finally, the simulation result can be the reference for financing institutes to evaluate customers’ financial status and taken into account the risk and given credit.
目 錄
中文摘要 ………………………………………………………… i
英文摘要 ………………………………………………………… iii
目錄 ……………………………………………………………… vi
表目錄 …………………………………………………………… viii
圖目錄 …………………………………………………………… ix
符號說明 ………………………………………………………… x
第一章 緒論 …………………………………………………… 1
1.1 研究動機 ……………………………………… 1
1.2 研究背景與發展概況 …………………………… 2
1.3 研究方法及目標 ………………………………… 4
1.4 論文架構 ……………………………………… 5
第二章 信用卡基本資料處理 ………………………………… 8
2.1 信用卡基本資料概述 …………………………… 8
2.2 輸入變數可直接判斷部分 ……………………… 8
2.3 輸入變數無法直接判斷部分 …………………… 10
第三章 支持向量學習模式 ………………………………… 12
3.1 簡介 ……………………………………………… 12
3.3 SVM 架構 ……………………………………… 13
第四章 自我建構能力之模糊類神經網路 …………………… 16
4.1 SONFIN 的架構 ……………………………… 16
4.2 SONFIN 的學習演算法 ……………………… 20
4.2.1 輸入、輸出空間的切割 ……………………… 20
4.2.2 模糊法則的建構 ……………………………… 24
4.2.3 推論部分架構的確認 ……………………… 25
4.2.4 網路參數的確認 …………………………… 26
第五章 信用摘要之識別 …………………………………… 29
5.1 信用摘要的意涵與內容 ……………………… 29
5.2 申請者信用識別 ……………………………… 30
5.2.1 數學模式的建立 ……………………………… 30
5.2.2 信用識別分析結果之特性探討 ……………… 31
5.2.3 信用異常權重函數之建立 …………………… 32
第六章 電腦模擬 ……………………………………………… 35
6.1 資料型態 ………………………………………… 35
6.1.1 輸入資料型態 ………………………………… 35
6.1.2 輸出資料型態 ………………………………… 43
6.2 模擬結果 ………………………………………… 43
6.2.1 發卡判斷率 …………………………………… 44
6.2.2 違約判斷率 …………………………………… 51
第七章 結論與未來展望 ……………………………………… 55
7.1 結論 …………………………………………… 55
7.2 未來展望 ……………………………………… 56
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