【摘要】：隨著農(nóng)村經(jīng)濟(jì)的飛快發(fā)展,農(nóng)村的信用貸款需求也跟著增多。農(nóng)村的信用貸款以農(nóng)戶小額信用貸款為主。而農(nóng)戶信用風(fēng)險(xiǎn)評(píng)估在農(nóng)戶小額貸款中至關(guān)重要,它的好壞制約著農(nóng)村經(jīng)濟(jì)的發(fā)展。因此對(duì)于農(nóng)戶小額貸款風(fēng)險(xiǎn)評(píng)估系統(tǒng)的研究十分必要。 本文首先介紹了農(nóng)戶信用評(píng)價(jià)的概念,以及農(nóng)戶小額信用貸款的概念,接著詳細(xì)分析了國(guó)內(nèi)外對(duì)農(nóng)戶小額信用貸款風(fēng)險(xiǎn)評(píng)估的研究,通過(guò)分析發(fā)現(xiàn)國(guó)內(nèi)在這方面的研究不如國(guó)外。由于人工智能在信用評(píng)價(jià)應(yīng)用中有很大優(yōu)勢(shì),在國(guó)外已經(jīng)廣泛使用人工智能技術(shù)。而BP神經(jīng)網(wǎng)絡(luò)技術(shù)是人工智能技術(shù)中的一個(gè)重要技術(shù),它有著較強(qiáng)的學(xué)習(xí)和自適應(yīng)能力、較好的內(nèi)在并行計(jì)算和存儲(chǔ),是一種穩(wěn)定的非線性方法等優(yōu)點(diǎn),所以BP神經(jīng)網(wǎng)絡(luò)在農(nóng)戶小額信用貸款風(fēng)險(xiǎn)評(píng)估的研究中也得到應(yīng)用。然而傳統(tǒng)的BP神經(jīng)網(wǎng)絡(luò)有著收斂速度慢、易陷入局部極小等不足。針對(duì)這些不足,人們先后提出了很多改進(jìn)的策略,比如加動(dòng)量項(xiàng)、自適應(yīng)學(xué)習(xí)速率、LM算法、人工免疫、遺傳算法、粒子群優(yōu)化算法等。 本文使用了一種新興的群體智能算法量子粒子群優(yōu)化算法(QPSO)改進(jìn)BP神經(jīng)網(wǎng)絡(luò)模型。量子粒子群優(yōu)化算法(QPSO)有著調(diào)節(jié)參數(shù)少、簡(jiǎn)單易實(shí)現(xiàn)的優(yōu)點(diǎn),并且有著較好的收斂性能和全局搜索能力,能在一定程度上能夠克服BP神經(jīng)網(wǎng)絡(luò)算法在收斂性能上的不足。 通過(guò)加入自適應(yīng)變異對(duì)量子粒子群優(yōu)化算法(QPSO)進(jìn)行改進(jìn),并取得了很好的效果。由于量子粒子群算法在早期收斂速度較快,所以在后期可能沒(méi)有達(dá)到全局最優(yōu)時(shí)已經(jīng)聚集到某一點(diǎn),形成局部極小。針對(duì)這一缺點(diǎn)加入自適應(yīng)變異對(duì)量子粒子群優(yōu)化算法(QPSO)進(jìn)行改進(jìn)。 本文改進(jìn)后的量子粒子群優(yōu)化算法(QPSO)通過(guò)優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的權(quán)值和閾值,從而得到改進(jìn)的BP神經(jīng)網(wǎng)絡(luò)模型。它與用遺傳算法等改進(jìn)的BP神經(jīng)網(wǎng)絡(luò)模型相比,能夠更有效的提高BP神經(jīng)網(wǎng)絡(luò)的收斂速度,防止陷入局部極小。 然后,將改進(jìn)后的BP神經(jīng)網(wǎng)絡(luò)模型應(yīng)用到農(nóng)戶小額信用貸款風(fēng)險(xiǎn)評(píng)估系統(tǒng)實(shí)驗(yàn)中。在仿真模擬實(shí)驗(yàn)中,從數(shù)據(jù)中隨機(jī)抽取5組數(shù)據(jù)集進(jìn)行實(shí)驗(yàn),然后再取這5組實(shí)驗(yàn)結(jié)果的平均值與用傳統(tǒng)BP神經(jīng)網(wǎng)絡(luò)進(jìn)行對(duì)比,我們可以很明顯的看出,經(jīng)過(guò)改進(jìn)的BP神經(jīng)網(wǎng)絡(luò)更能幫助我們提升信用評(píng)估過(guò)程中的精確性,錯(cuò)誤的可能性也可以降低。 本文最后從農(nóng)戶小額信用貸款風(fēng)險(xiǎn)評(píng)估的研究方面和改進(jìn)BP神經(jīng)網(wǎng)絡(luò)方面進(jìn)行了下一步展望。
[Abstract]:With the rapid development of rural economy, rural credit loan demand also increases. Rural credit loans to farmers mainly small credit loans. The credit risk assessment of farmers is very important in farmers' small loans, which restricts the development of rural economy. Therefore, it is necessary to study the risk assessment system of farmers' small loans. This paper first introduces the concept of peasant household credit evaluation and the concept of farmers small credit loan, and then analyzes the domestic and foreign research on the risk assessment of farmers small credit loan in detail. Through analysis, it is found that the domestic research in this area is not as good as that in foreign countries. Artificial intelligence (AI) has been widely used in foreign countries because of its great advantage in the application of credit evaluation. BP neural network technology is an important technology in artificial intelligence technology, it has strong learning and adaptive ability, better internal parallel computing and storage, it is a stable nonlinear method and so on. Therefore, BP neural network is also applied in the risk assessment of farmers' small credit loan. However, the traditional BP neural network has some shortcomings, such as slow convergence rate and easy to fall into local minima. To solve these problems, many improved strategies have been put forward, such as adding momentum term, adaptive learning rate, LM algorithm, artificial immune algorithm, genetic algorithm, particle swarm optimization algorithm and so on. In this paper, a new swarm intelligence algorithm, quantum particle swarm optimization (QPSO), is used to improve the BP neural network model. Quantum Particle Swarm Optimization (QPSO) has the advantages of less adjusting parameters, simple and easy to realize, and has better convergence performance and global search ability. It can overcome the shortcoming of BP neural network algorithm in convergence performance to a certain extent. The quantum particle swarm optimization (QPSO) algorithm (QPSO) is improved by adding adaptive mutation, and good results are obtained. Because the quantum particle swarm optimization (QPSO) converges fast in the early stage, it has gathered to a certain point and formed a local minima when the global optimization is not reached in the later stage. An adaptive mutation is added to improve the quantum particle swarm optimization (QPSO) algorithm (QPSO). The improved Quantum Particle Swarm Optimization (QPSO) algorithm (QPSO) obtains the improved BP neural network model by optimizing the weights and thresholds of the BP neural network. Compared with the improved BP neural network model such as genetic algorithm, it can improve the convergence speed of BP neural network more effectively and prevent it from falling into local minima. Then, the improved BP neural network model is applied to the risk assessment system of farmer's small credit loan. In the simulation experiment, five groups of data sets are randomly selected from the data to carry out the experiment, and then the average values of the five groups of experimental results are compared with the traditional BP neural network. The improved BP neural network can improve the accuracy of credit evaluation and reduce the possibility of error. In the end, this paper looks forward to the next step from the aspects of the risk assessment of farmer's small credit loan and the improvement of BP neural network.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F832.4;TP183

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