神经元网络在SAS中的实现（I）

大多数常见的人工神经元网络－多层感知器（ＭＬＰ），无非是非线性回归以及判别模型，其训练方法通常是适合大型并行机硬件实现的梯度下降算法。     

任意长度W变换的统一算法及其实现

任意长度Ｗ变换的统一算法及其实现曾泳泓，蒋增荣（国防科技大学）ＡＵＮＩＦＩＥＤＭＳＴＡＬＧＯＲＩＴＨＭＦＯＲＴＨＥＤＩＳＣＲＥＴＥＷＴＲＡＮＳＦＯＲＭＷＩＴＨＡＲＢＩＴＲＡＲＶＬＥＮＧＴＨ￥ＺｅｎｇＹｏｎｇ－ｈｏｎｇ；ＪｉａｎｇＺｅｎｇ－ｒｏｎｇ（７...     

关于随机优化算法的几点讨论

关于随机优化算法的几点讨论李晓莉，雷功炎（河南驻马店师专，北京大学数学系）ＳＯＭＥＤＩＳＣＵＳＳＩＯＮＳＡＢＯＵＴＳＴＯＣＨＡＳＴＩＣＯＰＴＩＭＩＺＡＴＩＯＮＡＬＧＯＲＩＴＨＭＳ￥ＬｉＸｉａｏ－ｌｉ（ＤｅｐａｒｔｍｅｎｔｏｆＭａｔｈｅｍａｔｉｃｓ，Ｚ...     

关于随机优化算法的几点讨论

关于随机优化算法的几点讨论李晓莉，雷功炎（河南驻马店师专，北京大学数学系）ＳＯＭＥＤＩＳＣＵＳＳＩＯＮＳＡＢＯＵＴＳＴＯＣＨＡＳＴＩＣＯＰＴＩＭＩＺＡＴＩＯＮＡＬＧＯＲＩＴＨＭＳ￥ＬｉＸｉａｏ－ｌｉ（ＤｅｐａｒｔｍｅｎｔｏｆＭａｔｈｅｍａｔｉｃｓ，Ｚ...     

数据变换模型的局部影响分析

数据变换模型的局部影响分析韦博成，史建清（东南大学数学系，南京２１００１８）ＬＯＣＡＬＩＮＦＬＵＥＮＣＥＡＮＡＬＹＳＩＳＦＯＲＲＥＧＲＥＳＳＩＯＮＴＲＡＮＳＦＯＲＭＡＴＩＯＮＭＯＤＥＬＳ￥ＷＥＩＢＯＣＨＥＮＧＡＮＤＳＨＩＪＩＡＮＧＩＮＧ（Ｄｅｐｅｒｔ...     

市场研究（I）：揭示竞争优势

ＳＡＳ系统提供了用于市场数据分析的许多方法，包括直观图示法和关联分析，这篇文章讨论直观图法及其ＳＡＳ系统中的实现。     

一类季节性整值自回归模型—SINAR（1）

一类季节性整值自回归模型──ＳＩＮＡＲ（１）李元杜金观，伍尤桂（石油大学数理系，东营２５７０６２）（中科院应用数学所，北京１０００８０）施久玉（哈尔滨船舶工程学院基础部，哈尔滨１５０００１）ＡＴＹＰＥＯＦＳＥＡＳＯＮＡＬＩＮＴＥＧＥＲ－ＶＡＬＵＥＤＡ...     

市场研究(Ⅰ):揭示竞争优势

ＳＡＳ系统提供了用于市场数据分析的许多方法，包括直观图示法和关联分析。这篇文章讨论直观图示法及其在ＳＡＳ系统中的实现。     

Glowinski区域分解算法的收敛性方程：Stokes方程

Ｇｌｏｗｉｎｓｋｉ区域分解算法的收敛性方程──Ｓｔｏｋｅｓ方程储德林，胡显承（清华大学应用数学系，北京１０００８４）ＴＨＥＣＯＮＶＥＲＧＥＮＣＥＯＦＧＬＯＷＩＮＳＫＩ＇ＳＤＯＭＡＩＮＤＥＣＯＭＰＯＳＩＴＩＯＮＡＬＧＯＲＩＴＨＭ──ＳＴＯＫＥＳＥＱＵＡ...     

一类非线性单调型问题的平行化算法

一类非线性单调型问题的平行化算法许学军，蒋美群（苏州大学数学系）ＭＲＡＬＬＥＬＡＬＧＯＲＩＴＨＭＳＦＯＲＡＮＯＮＬＩＮＥＡＲＭＯＮＯＴＯＮＥＰＲＯＢＬＥＭ￥ＸｕＸｕｅ－ｊｕｎ；ＪｉａｎｇＭｅｉ－ｑｕｎ（ＳｕｚｈｏｕＵｎｉｖｅｒｓｉｔｙ，Ｓｕｚｈｏｕ）...     

前馈神经网络和Logit回归的比较研究

近年来 ,前馈神经网络广泛地应用在 Logit回归作为标准统计方法的分析领域 .但却很少作它们之间的直接比较 ,本文是 Logit回归和前馈神经网络“比较研究”的一个尝试 ,说明了一些理论结果和特性 ,讨论了在它们的应用中碰到的一些实际问题 ,还进一步用分析的和模拟的两种方法研究了一些重要的渐近概念、过分拟合以及模型选择等问题 ,最后讨论并给出一些结论     

Forecasting and recombining time-series components by using neural networks

Operations and other business decisions often depend on accurate time-series forecasts. These time series usually consist of trend-cycle, seasonal, and irregular components. Existing methodologies attempt to first identify and then extrapolate these components to produce forecasts. The proposed process partners this decomposition procedure with neural network methodologies to combine the strengths of economics, statistics, and machine learning research. Stacked generalization first uses transformations and decomposition to pre-process a time series. Then a time-delay neural network receives the resulting components as inputs. The outputs of this neural network are then input to a backpropagation algorithm that synthesizes the processed components into a single forecast. Genetic algorithms guide the architecture selection for both the time-delay and backpropagation neural networks. The empirical examples used in this study reveal that the combination of transformation, feature extraction, and neural networks through stacked generalization gives more accurate forecasts than classical decomposition or ARIMA models.?Scope and Purpose.?The research reported in this paper examines two concurrent issues. The first evaluates the performance of neural networks in forecasting time series. The second assesses the use of stacked generalization as a way of refining this process. The methodology is applied to four economic and business time series. Those studying time series and neural networks, particularly in terms of combining tools from the statistical community with neural network technology, will find this paper relevant.     

A comparison of neural networks and linear scoring models in the credit union environment

The purpose of the present paper is to explore the ability of neural networks such as multilayer perceptrons and modular neural networks, and traditional techniques such as linear discriminant analysis and logistic regression, in building credit scoring models in the credit union environment. Also, since funding and small sample size often preclude the use of customized credit scoring models at small credit unions, we investigate the performance of generic models and compare them with customized models. Our results indicate that customized neural networks offer a very promising avenue if the measure of performance is percentage of bad loans correctly classified. However, if the measure of performance is percentage of good and bad loans correctly classified, logistic regression models are comparable to the neural networks approach. The performance of generic models was not as good as the customized models, particularly when it came to correctly classifying bad loans. Although we found significant differences in the results for the three credit unions, our modular neural network could not accommodate these differences, indicating that more innovative architectures might be necessary for building effective generic models.     

Simulating and visualizing neural networks with Mathematica

Artificial neural networks (ANNs) are both mathematical models of the neural basis of higher-order cognitive functions, such as learning, and adaptive variations of the general linear and nonlinear regression. Students of psychology and cognitive science typically encounter ANNs in both contexts of their studies, especially at the graduate level, however, many of these students do not possess the programming skills to write their own simulations to test their application as cognitive and statistical models. In this paper, simulations using the mathematical programming language Mathematica are used to develop appropriate visualizations of one the foundation topics in ANNs (understanding why linear associative networks cannot learn the nonlinearly separable XOR function). It is argued that Mathematica and similar high-level interpreted packages provide a more accessible environment for nonprogramming students to further their understanding of this key area of psychological science and mathematical modelling.     

Conflict analysis using Bayesian neural networks and generalized linear models

The study of conflict analysis has recently become more important due to current world events. Despite numerous quantitative analyses on the study of international conflict, the statistical results are often inconsistent with each other. The causes of conflict, however, are often stable and replicable when the prior probability of conflict is large. As there has been much conjecture about neural networks being able to cope with the complexity of such interconnected and interdependent data, we formulate a statistical version of a neural network model and compare the results to those of conventional statistical models. We then show how to apply Bayesian methods to the preferred model, with the aim of finding the posterior probabilities of conflict outbreak and hence being able to plan for conflict prevention.     

Bayesian neural network learning for repeat purchase modelling in direct marketing

We focus on purchase incidence modelling for a European direct mail company. Response models based on statistical and neural network techniques are contrasted. The evidence framework of MacKay is used as an example implementation of Bayesian neural network learning, a method that is fairly robust with respect to problems typically encountered when implementing neural networks. The automatic relevance determination (ARD) method, an integrated feature of this framework, allows us to assess the relative importance of the inputs. The basic response models use operationalisations of the traditionally discussed Recency, Frequency and Monetary (RFM) predictor categories. In a second experiment, the RFM response framework is enriched by the inclusion of other (non-RFM) customer profiling predictors. We contribute to the literature by providing experimental evidence that: (1) Bayesian neural networks offer a viable alternative for purchase incidence modelling; (2) a combined use of all three RFM predictor categories is advocated by the ARD method; (3) the inclusion of non-RFM variables allows to significantly augment the predictive power of the constructed RFM classifiers; (4) this rise is mainly attributed to the inclusion of customer/company interaction variables and a variable measuring whether a customer uses the credit facilities of the direct mailing company.     

Role and Results of statistical methods in protein fold class prediction

Statistical methods of discrimination and classification are used for the prediction of protein structure from amino acid sequence data. This provides information for the establishment of new paradigms of carcinogenesis modeling on the basis of gene expression. Feed forward neural networks and standard statistical classification procedures are used to classify proteins into fold classes. Logistic regression, additive models, and projection pursuit regression from the family of methods based on a posterior probabilities; linear, quadratic, and a flexible discriminant analysis from the class of methods based on class conditional probabilities, and the nearest-neighbors classification rule are applied to a data set of 268 sequences. From analyzing the prediction error obtained with a test sample (n = 125) and with a cross validation procedure, we conclude that the standard linear discriminant analysis and nearest-neighbor methods are at the same time statistically feasible and potent competitors to the more flexible tools of feed forward neural networks. Further research is needed to explore the gain obtainable from statistical methods by the application to larger sets of protein sequence data and to compare the results with those from biophysical approaches.     

一类递归小波神经网络的稳定性研究

在小波神经网络（WNNs）和递归神经网络（RNNs）的基础上,提出了一类递归小波神经网络（RWNNs）模型,它具有两种网络模型的优点A·D2根据Liapunov渐近稳定理论,对该模型的渐近稳定性进行了研究,并给出了相关的定理和公式．仿真结果表明该模型对非线性动态系统有良好的辨识效果．     

Fuzzy regression analysis using neural networks

In this paper, we propose simple but powerful methods for fuzzy regression analysis using neural networks. Since neural networks have high capability as an approximator of nonlinear mappings, the proposed methods can be applied to more complex systems than the existing LP based methods. First we propose learning algorithms of neural networks for determining a nonlinear interval model from the given input-output patterns. A nonlinear interval model whose outputs approximately include all the given patterns can be determined by two neural networks. Next we show two methods for deriving nonlinear fuzzy models from the interval model determined by the proposed algorithms. Nonlinear fuzzy models whose h-level sets approximately include all the given patterns can be derived. Last we show an application of the proposed methods to a real problem.     

Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis

In this paper, we present a general framework for understanding the role of artificial neural networks (ANNs) in bankruptcy prediction. We give a comprehensive review of neural network applications in this area and illustrate the link between neural networks and traditional Bayesian classification theory. The method of cross-validation is used to examine the between-sample variation of neural networks for bankruptcy prediction. Based on a matched sample of 220 firms, our findings indicate that neural networks are significantly better than logistic regression models in prediction as well as classification rate estimation. In addition, neural networks are robust to sampling variations in overall classification performance.