农户信用评估系统的设计与运用研究

农户信用评估的研究对推动农村消费信用的发展，促进农村经济良好运行十分重要。本在构造农户信用评估的指标体系的基础上，提出了农户信用评估神经网络模型的算法，利用实际搜集到的农户资料进一步建立了农户信用评估模型，继而构造了农户信用评估系统，并举例说明了该系统的实际运用，以期能为农村经济发展中的农户信用评价及相关研究提供一丝基础性启发。     

基于BP神经网络的P2P网贷借款人信用评估

评估借款人信用是P2P网贷公司控制风险的重要步骤,对于网贷公司的正常运行有着极其重要的意义。论文参考商业银行信用指标体系并根据P2P网贷自身特点,建立了P2P网贷借款人的信用评估指标体系。根据建立的指标体系构建相应的BP神经网络模型,并利用一步正切法进行优化。然后选取具有代表性的P2P网贷平台的相关数据,对该模型进行训练和仿真,证明了该模型对P2P网贷平台的风险控制起到一定的作用。     

基于BP神经网络的企业信用评估模型

本文研究了企业信用评估中的模型问题.以商业企业为例,阐述了基于BP神经网络的信用评估模型的原理,通过建立指标体系,讨论基于BP神经网络的评价模型的实现,对模型的不足进行了分析,并提出改进建议.     

基于BP神经网络的行业间中小企业信用评估模型及应用

首先构建了行业间中小企业信用评估指标体系,然后利用安徽省不同行业的800家中小企业调查数据,将其分为训练样本集和测试样本集,对BP神经网络的构造进行讨论,确定BP神经网络的算法,建立起基于BP神经网络的行业间信用评估模型,并代入2003年度全国农业和工业的部分分行业数据进行实证,并对仿真结果做出分析,指出造成农、工行业信用较大差距的原因,并提出加强农业行业信用建设的建议.     

特征选择方法在信用评估指标选取中的应用

在信用评分模型中所运用的指标变量对模型的表现有重要的影响,指标选取方法的科学化规范化水平有待于进一步提高。本文研究了机器学习领域的特征选择方法在定量确定信用评分模型指标体系上的应用。以实际信用评估问题为例,对四种特征选择方法(ReliefF方法、基于相关性的方法、基于一致性的方法和包裹性)进行了比较试验,验证了特征选择方法可以在精简性、速度和准确率三个方面提高信用评分模型的表现。其中基于一致性的方法和包裹法表现优于Reli-efF方法和基于相关性的方法。     

基于BP神经网络误差函数优化算法的武器装备承制商信用评价模型研究

信用评价是选择武器装备承制商的重要手段.以国标为基础,结合承制商具体情况确定了信用评价指标体系.分析了传统信用评价方法的不足,对经典BP神经网络的误差函数进行优化,优化后的网络模型收敛速度更快,预测精度更高.构建BP神经网络武器装备承制商信用评价模型,仿真实验表明武器装备承制商信用评价可以选用BP神经网络模型.     

多层感知器信用评模型及预警研究

本文利用多层感知器 ( MLP)原理建立神经网络信用评价模型 ,用来对我国 2 0 0 0年 1 0 6家上市公司进行信用评级 ,并进一步对我国 2 0 0 1年公布的 1 3家预亏公司进行预警研究 .按照各上市公司的经营状况分为“好”、“差”两类 ,每一类由 5 3家上市公司构成数据样本 .对于每一家上市公司 ,主要考虑其经营状况的四个财务指标 :每股收益 ,每股净资产 ,净资产收益率和每股现金流量 .仿真结果表明 ,本文所建立的神经网络信用评价模型有很高的分类准确率 ,达到 98.1 1 % .又由于该信用评价模型有很强的适应能力 ,故可以进一步用来对企业的财务危机进行预警研究 .预警实证分析表明 ,该信用评价模型对我国 2 0 0 1年公布的 1 3家预亏公司进行预警分析 ,预警准确率达到 1 0 0 % .此外 ,文中还给出 MLP网络模型的学习算法和步骤     

企业信用评估指标体系与评价方法研究

市场经济是信用经济,但当前我国经济运行中失信问题突出,诚信建设尤其是企业信用建设亟待加强。信用体系建设的核心是选择科学的信用评估指标体系与评估方法。本文从企业征信的基本模式、企业信用评估的指标体系和评估模型三个方面阐述信用评估方法,同时结合我国实际对信用体系建设提出了建议。     

行业内中小企业信用评估模型及应用

在对中国中小企业特点的分析基础上,建立起三层次的行业内中小企业信用评估指标体系。并将其中的财务状况指标体系单独列出,通过对20家安徽省农资中小企业的调查,以此行业为例,利用主成分分析法筛选变量.简化原指标系统,进一步利用Logistic函数对财务状况指标计算公式进行了修正.然后通过层次分析法对各层指标权重进行计算,建立起行业内中小企业信用评估模型.同时根据商业银行的放贷目的,对模型进行了进一步讨论,在确定银行贷款临界概率的基础上,利用中小企业信用评估模型建立起银行贷款决策模型,并代入2005年度上半年合肥丰乐种业股份有限公司的数据进行了实证分析.     

基于分类回归树的个人信用客观评分指标体系(英文)

本文提出了一种客观的个人信用指标体系.首先利用分类回归树量化每个指标对信用状况的影响程度,并以此量化值为每个指标设置不同的评分权重;然后通过定义风险度量值来确定指标中各个取值的评分,进而建立了新的评估指标体系.通过选取现实样本数据对指标体系做了实证分析,分析结果表明,新建的指标体系能很好地对借款人进行风险评价.     

多层前向正则模糊神经网络的逼近能力

Abstract. Four-layer feedforward regular fuzzy neural networks are constructed. Universal ap-proximations to some continuous fuzzy functions defined on (R)“ by the four-layer fuzzyneural networks are shown. At first,multivariate Bernstein polynomials associated with fuzzyvalued functions are empolyed to approximate continuous fuzzy valued functions defined on eachcompact set of R“. Secondly,by introducing cut-preserving fuzzy mapping,the equivalent condi-tions for continuous fuzzy functions that can be arbitrarily closely approximated by regular fuzzyneural networks are shown. Finally a few of sufficient and necessary conditions for characteriz-ing approximation capabilities of regular fuzzy neural networks are obtained. And some concretefuzzy functions demonstrate our conclusions.     

Neural networks for a class of bi-level variational inequalities

In this paper, a class of bi-level variational inequalities for describing some practical equilibrium problems, which especially arise from engineering, management and economics, is presented, and a neural network approach for solving the bi-level variational inequalities is proposed. The energy function and neural dynamics of the proposed neural network are defined in this paper, and then the existence of the solution and the asymptotic stability of the neural network are shown. The simulation algorithm is presented and the performance of the proposed neural network approach is demonstrated by some numerical examples.     

Mittag-Leffler stability and adaptive impulsive synchronization of fractional order neural networks in quaternion field

Fractional order quaternion-valued neural networks are a type of fractional order neural networks for which neuron state, synaptic connection strengths, and neuron activation functions are quaternion. This paper is dealing with the Mittag-Leffler stability and adaptive impulsive synchronization of fractional order neural networks in quaternion field. The fractional order quaternion-valued neural networks are separated into four real-valued systems forming an equivalent four real-valued fractional order neural networks, which decreases the computational complexity by avoiding the noncommutativity of quaternion multiplication. Via some fractional inequality techniques and suitable Lyapunov functional, a brand new criterion is proposed first to ensure the Mittag-Leffler stability for the addressed neural networks. Besides, the combination of quaternion-valued adaptive and impulsive control is intended to realize the asymptotically synchronization between two fractional order quaternion-valued neural networks. Ultimately, two numerical simulations are provided to check the accuracy and validity of our obtained theoretical results.     

Stability analysis of Takagi–Sugeno fuzzy Cohen–Grossberg BAM neural networks with discrete and distributed time-varying delays

In this paper, the global asymptotic stability problem of Takagi–Sugeno (TS) fuzzy Cohen–Grossberg Bidirectional Associative Memory neural networks (FCGBAMNNs) with discrete and distributed time-varying delays is considered. A novel LMI-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of FCGBAMNNs which are represented by TS fuzzy models. Our results can be easily verified and are also less restrictive than previously known criteria and can be applied to Cohen–Grossberg neural networks, recurrent neural networks and cellular neural networks. Finally, the proposed stability conditions are demonstrated with a numerical example.     

A note on knowledge discovery using neural networks and its application to credit card screening

We address an important issue in knowledge discovery using neural networks that has been left out in a recent article “Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem” by Sexton et al. [R.S. Sexton, S. McMurtrey, D.J. Cleavenger, Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem, European Journal of Operational Research 168 (2006) 1009–1018]. This important issue is the generation of comprehensible rule sets from trained neural networks. In this note, we present our neural network rule extraction algorithm that is very effective in discovering knowledge embedded in a neural network. This algorithm is particularly appropriate in applications where comprehensibility as well as accuracy are required. For the same data sets used by Sexton et al. our algorithm produces accurate rule sets that are concise and comprehensible, and hence helps validate the claim that neural networks could be viable alternatives to other data mining tools for knowledge discovery.     

Information processing,memories, and synchronization in chaotic neural network with the time delay

Information processing and two types of memory in an analog neural network model with time delay that produces chaos similar to the human and animal EEGs are considered. There are two levels of information processing in this neural network: the level of individual neurons and the level of the neural network. Similar to the state of brain, the state of chaotic neural network is defined. It is characterized by two types of memories (memory I and memory II) and correlation structure between the neurons. In normal (unperturbed) state, the neural network generates chaotic patterns of averaged neuronal activities (memory I) and patterns of oscillation amplitudes (memory II). In the presence of external stimulation, the activity patterns change, showing changes in both types of memory. As in experiments on stimulation of the brain, the neural network model shows synchronization of neuronal activities due to stimulus measured by Pearson's correlation coefficient. An increase in neural network asymmetry (increase of the neural network excitability) leads to the phenomenon similar to the epilepsy. Modeling of brain injury, Parkinson's disease, and dementia is performed by removing and weakening interneuron connections. In all cases, the chaotic neural network shows a decrease of the degree of chaos and changes in both types of memory similar to those observed in experiments with healthy human subjects and patients with Parkinson's disease and dementia. © 2005 Wiley Periodicals, Inc. Complexity 11:39–52, 2005     

Exponential stability of discrete-time neural networks with delay and impulses

In this paper, we investigate the exponential stability of discrete-time neural networks with impulses and time-varying delay. The discrete-time neural networks are derived by discretizing the corresponding continuous-time counterparts with different discretization methods. The impulses are classified into three classes: input disturbances, stabilizing and “neutral” type - the impulses are neither helpful for stabilizing nor destabilizing the neural networks, and then by using the excellent ideology introduced recently by Chen and Zheng [W.H. Chen, W.X. Zheng, Global exponential stability of impulsive neural networks with variable delay: an LMI approach, IEEE Trans. Circuits Syst. I 56 (6) (2009) 1248-1259], the connections between the impulses and the utilized Lyapunov function are fully explored with respect to each type of impulse. Novel techniques that used to realize the ideology in discrete-time situation are proposed and it is shown that they are essentially different from the continuous-time case. Several criteria for global exponential stability of the discrete-time neural networks are established in terms of matrix inequalities and based on these theoretical results numerical simulations are given to compare the capability of different discretization methods.     

Improved global exponential stability criteria of cellular neural networks with time-varying delays

In this paper, the existence and uniqueness of the equilibrium point and stability of the cellular neural networks (CNNs) with time-varying delays are analyzed and proved. Several global exponential stability conditions of the neural networks are obtained by the delay differential inequality and matrix measures approach. The obtained results are extensions of the earlier literature. The approach used in this paper is also suitable for delayed Hopfield neural networks and delayed bi-directional associative memory neural networks whose activation functions are often nondifferentiable or unbounded. Two simulation examples in comparison to previous results in literature are shown to check the theory in this paper.     

基于蚁群神经网络的财务危机预警方法

为了克服神经网络财务危机预警方法收敛慢、不收敛和网络结构难以确定等缺陷,提出了基于蚁群算法的改进神经网络财务危机预警方法。将神经网络模型的结构和参数进行编码,利用蚁群算法确定若干个神经网络模型的结构和参数,然后通过评价函数得到神经网络的最佳结构,最后通过BP算法训练该神经网络,得到神经网络财务危机预警模型。验证结果表明,该模型结构简单、预警精度高。     

用在线梯度法训练积单元神经网络的收敛性分析

<正>1引言仅由加和单元构成的传统前向神经网络已经广泛应用于模式识别及函数逼近等领域.但在处理比较复杂的问题时,这种网络往往需要补充大量的隐节点,这样就不可避免地增