bp算法在信用風(fēng)險(xiǎn)分析中的應(yīng)用(完整版)
【正文】 評(píng)級(jí)機(jī)構(gòu)根據(jù)科學(xué)的指標(biāo)體系，采用嚴(yán)謹(jǐn)?shù)姆治龇椒ǎ\(yùn)用簡(jiǎn)明的文字符號(hào)，對(duì)被評(píng)級(jí)單位履行經(jīng)濟(jì)責(zé)任的能力及其可信任程度進(jìn)行客觀公正的評(píng)價(jià)，并確定其信用等級(jí)的一種經(jīng)濟(jì)活動(dòng)。設(shè)表示網(wǎng)絡(luò)訓(xùn)練次數(shù)，此處分別取390，800（下同）。參考文獻(xiàn)1. K. Y. Tam and M. Kiang. Predicting bank failures: A neural network approach. [J]. Management Science，1992, 38(7)：927947. 2. H. L. Jensen. Using neural networks for credit scoring. [J]. Managerial Finance, 1992, 18 (6), 1526.3. P. Coats and L. Fant. Recoganizing financial distress patterns using a neural network tool. [J]. Financial Management, 1993, 3: 142155.4. . Altman. Corporate distress diagnosis: parisons using linear discriminant analysis and neural netwoeks (the Italian experience). [J]. Banking and Finance, 1994, 18:505529.5. R. R. Hashemi, L. A. Le Blanc, C. T. Rucks and A. Rajaratnam. A hybrid intelligent system for predicting bank holding structrure. [J]. European Journal of Operational Research, 1998, 109: 390402.6. David West. Neural network credit scoring models. [J]. Computers amp。將以上兩訓(xùn)練方式的樣本輸出值與原目標(biāo)值的最大誤差絕對(duì)值列于表1中。建立貸款企業(yè)的準(zhǔn)確審
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