论郑临川古典诗词的精神向度

郑临川先生的古典诗词在理想追求、爱国情怀、社会责任、读书治学等方面都深深地打上了时代烙印。他的诗词大都是人生旅程的写照,在对时势无奈的痛苦中发出感时哀世之吟,从而抒怀言志,表达强烈的爱国之情;其诗歌无论纪游、赠别、思乡、怀人,多呈离乱的悲凉心境,折射出知识分子发自内心深处的时代感怀。无论是刚接触现实融入社会的歌吟之作,还是表现漂泊流离、前途渺茫的无端伤感,抑或后来反映教书育人的心中吐蕊,以及人生起伏的情感激荡,无不透露着自觉而强烈的社会责任。老一辈知识分子那种坚定的爱国热情和自觉的社会责任,正是中华民族世代薪火相传的精神认同。     

Back propagation neural networks and multiple regressions in the case of heteroskedasticity

This paper compares the performance between regression analysis and a clustering based neural network approach when the data deviates from the homoscedasticity assumption of regression. Heteroskedasticity is a problem that arises in linear regression due to the unequal error variances. One of the methods to deal heteroskedasticity in classical regression theory is weighted least-square regression (WLS). In order to deal the problem of heteroskedasticity, backpropagation neural network is applied. In this context, an algorithm is proposed which is based on robust estimates of location and dispersion matrix that helps in preserving the error assumption of the linear regression. Analysis is carried out with appropriate designs using simulated data and the results are presented.     

Democratic demand for a social Europe? Preferences of the European citizenry

Within the literature on European integration there is a widespread assumption that Europe is in need of intensified and more effective supranational social policy cooperation. However, on the political level it is doubtful whether such measures are welcomed by the national electorates. This article addresses this issue empirically by asking whether there is public demand for promoting greater European welfare policy cooperation and what are the determinants of such a demand. The data source used is the Eurobarometer survey 2000. A number of hypotheses dealing with socio-structural differences, the effects of welfare regime types, the subjective evaluation of the integration process and the role of identity will be scrutinised. Overall, the results indicate that at the attitudinal 'grass root' level there is no unequivocal support for a European welfare responsibility and that some fundamental cleavages are present. It is the regional and cultural aspects, especially, which turn out to be having an effect and to be influencing future political conflicts. A common European welfare arrangement, therefore, cannot be regarded as a solution to the problems the European Union is facing; rather it will raise new and severe problems of finding social and political support.     

L'adoption par l'OIT d'une approche fondée sur la durabilité et ses conséquences sur le droit d'expression collective des travailleurs

L'action de l'OIT pour le développement durable s'est d'abord organisée autour des notions de «transition juste» et d'«emplois verts», en lien avec la protection de l'environnement. Aujourd'hui, les objectifs du développement durable devraient permettre à l'Organisation de se saisir pleinement de la dimension sociale du développement durable, grâce à l'ODD 8, qui mentionne le travail décent, et à la cible 16.7 sur la participation à la prise de décisions. L'auteure examine comment l'OIT pourrait promouvoir l'expression collective des travailleurs dans le contexte du débat sur un «avenir du travail durable» et cherche à déterminer qui doit avoir un droit d'expression et comment celui-ci doit s'exercer.     

应用混合神经网络和遗传算法的期权价格预测模型

隐含波动率是指在市场中观察的期权价格所蕴涵的波动率.提出了一种加权的隐含波动率作为混合神经网络的输入变量,建立了混合神经网络和遗传算法相结合的期权价格预测模型,通过遗传算法来优化神经网络的结构和获得隐含波动率的权重.在对香港金融衍生品市场的实证中表明,本文模型在预测结果上要优于传统的Black-Scholes模型.     

遗传算法优化神经网络及信用评价研究

研究关于公司神经网络信用评估问题的现状,提出遗传算法辅助网络训练策略(优化后的网络称为进化网络),克服传统网络建模中产生的局部极小缺陷。建立了适合于我国商业企业的信用评分指标体系;然后依据该指标体系建立了基于进化神经网络的信用评估模型;最后,利用样本公司实际指标数据对该模型的评分效果进行了比较研究。     

A comparison of Japanese failure models: Hybrid neural networks,logit models,and discriminant analysis

This article looks at the ability of a relatively new technique, hybrid artificial neural networks (ANNs), to predict Japanese banking and firm failures. These models are compared with traditional statistical techniques and conventional ANN models. The results suggest that hybrid neural networks outperform all other models in predicting failure for one year prior to the event. This suggests that for researchers, policymakers, and others interested in early warning systems, the hybrid network may be a useful tool for predicting banking and firm failures.     

TEI@I方法论及其在外汇汇率预测中的应用

基于TE I@I方法论的理论框架,构建了一个基于TE I@I方法论的外汇汇率预测模型。在此模型中,传统的经济计量模型用于处理外汇汇率的主要趋势,人工神经网络技术用于分析外汇汇率的非线性,而文本挖掘和专家系统用于处理外汇市场中的突现性和不稳定性。最后,基于集成的思想,利用支持向量回归技术对上述3个部分进行非线性集成,从而获得一个更为精确的预测结果。通过实证方法验证了基于TE I@I方法论的外汇汇率预测模型的有效性。     

Universal versus contextual effects on TQM: a triangulation study using neural networks

The objective of this study is to extend previous research on total quality management (TQM)-context-performance relationships and ‘fit’ using multiple methods. We combine artificial neural networks (ANNs) with structural equation modelling (SEM) to analyse several hypotheses and propositions. This is the first study in this area of research that utilises ANNs and a triangulation technique in the presence of several contextual factors. The SEM analyses suggest that company size and industry type may have contingency effects on some of the TQM practices and/or TQM-performance relationships. However, the ANN models have shown that these two contingency factors do not moderate TQM outcomes, implying that all organisations can benefit from TQM regardless of size and type. As well, these models show that formal TQM implementation and/or ISO certifications do not add any predictive power to the ANN models except in one case: TQM implementation and/or ISO certification added to organisational effectiveness and customer results to predict financial and market (F&M) results. The results further indicate that even though implementing TQM alone has a bigger impact on F&M results than obtaining ISO certification alone, combining the two will have an even greater impact on these results. Joint implementation leads to greater improvements in organisational effectiveness, which, in turn, has a positive effect on customer results and consequently F&M results. This is a unique finding within the context of moderator effects on TQM-performance relationships.     

基于贝叶斯正则化的TDBPNN模型在中国外贸预报中的应用及评估

基于相空间重构的非线性预报思想,建立一个时滞的BP神经网络模型(TDBPNN),采用贝叶斯正则化方法提高BP网络的泛化能力,并将该模型应用于中国进出口贸易的预测,结果证明改进的TDBPNN模型具有较好的泛化能力,准确拟合了进出口贸易发展的历史值和趋势。区别于一般的预测评价,认为非线性预测不仅要注重数据拟合和精度改进,而且应该能够反映被预报系统的非线性特征。在分析模型预测精度的同时,通过计算拟合序列和原序列的非线性特征量进行模型评价,证实预测模型能够合理地“捕捉”到产生原序列的非线性系统的动力学特征。