模糊類神經網路理論於信用之識別

Abstract

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.