共查询到19条相似文献,搜索用时 171 毫秒
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对信用风险、信用评分进行了分析,在综合分析国内外企业信用评分指标体系的基础上,结合我国企业信用评分的特点,建立了适合我国企业信用评价的指标体系。结合国内外相关研究的现状与进展,及信用评分本身所具有的特点,建立了基于径向基函数神经网络的信用评分模型,利用现有数据分别进行判别和分析,研究其计算结果与实际情况的差距,然后使用改进的RBFNN学习算法,对径向基函数神经网络进行了学习训练,得到了令人满意的评价结果。利用该模型建立的评分系统具有进一步研究和推广应用的价值。 相似文献
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基于径向基函数神经网络的泥石流危险性评价* 总被引:2,自引:0,他引:2
泥石流危险性的主要评价指标与危险程度之间有着某种复杂的非线性的关系,通常采用统计分析、模糊评价、BP神经网络等评价方法,但这些方法均存在不足之处,难以进行准确评价。为了克服以上方法的不足,结合泥石流危险性评价指标,建立了基于径向基函数神经网络的泥石流危险性评价模型,并将该模型结果与BP神经网络的评价结果进行了对比。实验结果表明,径向基函数神经网络的模拟结果比BP神经网络更接近测量数据,精度更高,训练所需时间更少。因此,径向基函数神经网络经过充分训练后,能够较为准确地对泥石流的危险性进行评价,具有较好的应用 相似文献
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提出了一种改进的径向基函数(RBF)神经网络,该神经网络以模糊系统模型为基础。首先利用减法聚类算法确定径向基函数的中心数,然后通过模糊C均值聚类算法优化基函数中心与宽度,最后依据样本数据的聚类结果设计RBF神经网络并进行训练。将该神经网络应用于网球队运动员的竞技状态的预测。仿真结果表明:该算法先进有效、具有较高的精度,用其建立的模型具有较强的实用性。 相似文献
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介绍了一种三层径向基函数神经网络,其学习算法采用正交最小二乘算法.首先根据正交最小二乘算法得到径向基函数神经网络的结构;然后对该网络的权值进行训练使它逼近给定的函数.为了验证径向基函数神经网络所具有的对任意非线性映射的任意逼近能力和自学习、自适应能力,以两关节机械手为辨识对象来进行实验研究.实验结果表明,该径向基函数神经网络具有良好的模型学习和逼近能力,并且学习速度快、收敛性好、鲁棒性强,尤其适合于具有连续线性与非线性对象的复杂系统的控制实时性要求. 相似文献
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为了建立国民经济生产总值(GDP)神经网络预测模型,构造了双层网络结构的基于径向基网络,通过学习训练,确定径向基数神经网络参数和结构。仿真结果表明,生成的径向基函数模型应用于国民经济预测比BP神经网络模型具有更高的预测精度和良好的泛化能力。 相似文献
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基于RBF神经网络的产品概念设计方案评价 总被引:1,自引:0,他引:1
分析了现有评价方法存在的问题,利用Matlab神经网络工具箱构建了RBF网络模型,并以冰箱为实例进行评价.RBF神经网络采用监督学习算法和正交最小平方(OLS)算法决定基函数的中心、方差以及隐含层到输出层的权值.与BP神经网络模型的评价结果对比,建立的RBF神经网络评价模型具有更高的预测精度,收敛速度更快. 相似文献
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利用微种群遗传算法,结合性能优越的径向基函数神经网络,建立了适用于散乱数据曲面重建的径向基函数网络模型.采用微种群遗传算法完成对神经网络的权值优化,可避免早熟收敛,且有较快的收敛速度.实验结果表明,用这种方法解决散乱数据点的重建问题,具有较高的精度. 相似文献
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本文建立了一种基于RBF神经网络的声纹识别系统。提取了Mel频率倒谱系数及其一阶差分参数,利用时间规整网络对所得参数进行了有效的简化,运用RBF神经网络对声纹特征参数进行了训练和识别。Matlab的实验结果表明,RBF神经网络在声纹识别中具有良好的发展前景。 相似文献
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Venkatanareshbabu Kuppili Diwakar Tripathi Damodar Reddy Edla 《Computational Intelligence》2020,36(2):402-426
Credit score classification is a prominent research problem in the banking or financial industry, and its predictive performance is responsible for the profitability of financial industry. This paper addresses how Spiking Extreme Learning Machine (SELM) can be effectively used for credit score classification. A novel spike-generating function is proposed in Leaky Nonlinear Integrate and Fire Model (LNIF). Its interspike period is computed and utilized in the extreme learning machine (ELM) for credit score classification. The proposed model is named as SELM and is validated on five real-world credit scoring datasets namely: Australian, German-categorical, German-numerical, Japanese, and Bankruptcy. Further, results obtained by SELM are compared with back propagation, probabilistic neural network, ELM, voting-based Q-generalized extreme learning machine, Radial basis neural network and ELM with some existing spiking neuron models in terms of classification accuracy, Area under curve (AUC), H-measure and computational time. From the experimental results, it has been noticed that improvement in accuracy and execution time for the proposed SELM is highly statistically important for all aforementioned credit scoring datasets. Thus, integrating a biological spiking function with ELM makes it more efficient for categorization. 相似文献
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Natasa Sarlija Mirta Bensic Marijana Zekic-Susac 《Expert systems with applications》2009,36(5):8778-8788
The paper deals with the problem of predicting the time to default in credit behavioural scoring. This area opens a possibility of including a dynamic component in behavioural scoring modelling which enables making decisions related to limit, collection and recovery strategies, retention and attrition, as well as providing an insight into the profitability, pricing or term structure of the loan. In this paper, we compare survival analysis and neural networks in terms of modelling and results. The neural network architecture is designed such that its output is comparable to the survival analysis output. Six neural network models were created, one for each period of default. A radial basis neural network algorithm was used to test all six models. The survival model used a Cox modelling procedure. Further, different performance measures of all models were discussed since even in highly accurate scoring models, misclassification patterns appear. A systematic comparison ‘3 + 2 + 2’ procedure is suggested to find the most effective model for a bank. Additionally, the survival analysis model is compared to neural network models according to the relative importance of different variables in predicting the time to default. Although different models can have very similar performance measures they may consist of different variables. The dataset used for the research was collected from a Croatian bank and credit customers were observed during a 12-month period. The paper emphasizes the importance of conducting a detailed comparison procedure while selecting the best model that satisfies the users’ interest. 相似文献
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研究企业信用风险评估准确性问题,企业存在产品质量、不良贷款等信用风险问题,企业信用风险是多种因素的综合结果,存在着不确定、非线性、随机性等特点,无法建立确定数学评估模型。只能根据专家评估指标为依据。为了提高企业信用风险评估准确率,提出一种BP神经网络的企业信用风险评估方法。先采用层次分析法构建风险评估指标体系,再用专家系统对评估指标进行量化打分,最后采用BP神经网络对企业信用风险指标进行非线性学习,并对企业信用风险等级进行评估。实验结果表明,BP神经网络的企业信用风险评估模模型能显著提高评估准确率,并能够反映企业信用风险的随机性变化特点,使评估结果更加符合实际情况,为企业信用风险评估提供了参考。 相似文献
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针对汽油机进气流量的多维非线性特性,提出了一种混沌径向基(RBF)神经网络的汽油机进气流量预测模型。证明了汽油机进气流量时间序列具有混沌特性,对采集的原始数据进行相空间重构,利用RBF神经网络对重构后的数据进行训练和预测,并利用混沌算法确定输出层连接权值和隐含层高斯函数径向基中心,使其达到全局最优,加快了RBF神经网络的收敛速度。仿真结果表明,与空气进气流量平均值模型、RBF神经网络预测模型比较,该模型具有更高的预测精度,为精确及时测试汽油机进气流量提供了一种全新的软件测量方法。 相似文献
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This paper describes four neural networks multilayer perceptron (MLP) network, Elman network, NARXSP network and radial basis
function (RBF) network. Neural networks are applied for identification and control of DC servo motor and benchmark nonlinear
system. Number of epochs required and time taken to train the controller are shown in the form of bar plots for four neural
networks. Levenberg-Marquardt algorithm is used for training the controller using neural network toolbox in MATLAB. Each neural
network controller is run ten times. Their performances are compared for each run in terms of number of epochs required and
time taken to train each controller for tracking a reference trajectory. 相似文献
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RBF网络光度法与BP网络光度法的比较 总被引:3,自引:4,他引:3
目的:探讨神经网络光度法用于复方制剂的含量测定。方法:训练集为按L25(5^6)正交表制备的25组标准混合液的吸光度数据和各组分的浓度数据,混合液中各组分的5个浓度水平分别为80%、90%、100%,110%和120%。预报集采用复方制剂的吸光度数据。网络的输入为混合物的吸光度,网络的输出为各组分的浓度。分别用径向基函数网络和Levenberg-Marqurdt优化算法的BP网络处理数据。结果:复方阿司匹林片和联磺甲氧苄啶片的紫外分光光度法测定结果表明,径向基函数网络在网络训练时间和测定精度等方面好于Levenberg-Marqurdt优化算法的BP网络。结论:径向基函数网络光度法测定复方制剂简便,准确。 相似文献