An approach to efficient business intelligent system for financial prediction

A concept of business intelligent system for financial prediction is considered in this paper. It provides data needed for fast, precise and good business decision support to all levels of management. The aim of the project is the development of a new online analytical processing oriented on case-based reasoning (CBR) where a previous experience for every new problem is taken into account. Methodological aspects have been tested in practice as a part of the management information system development project of “Novi Sad Fair”. A case study of an improved application of CBR in prediction of future payments is discussed in the paper. This paper is originally presented at The International Conference on Hybrid Information Technology 2006, at the special session on “Intelligent Information Systems for Financial Engineering”, November 2006 in Cheju Island, Korea.     

消费品说明中警示信息有效性的工效学研究(Ⅰ)——警示信息对消费者危险感受与商品危险性之间关系的影响

该研究采用问卷法，通过对３０种消费品的调查，分析了消费者对消费品说明中警示信息的阅读倾向性，消费者的危险感受、消费者安全使用商品的自信度、遵守警示信息的可能性、主观危险估计和客观危险性这几项因素之间的关系。结果表明：消费者的危险感受与阅读警示信息的倾向性、遵守警告的要求之间存在高度的一致性；且与自信度呈负相关，所以可以通过研究消费者的危险感受来研究警示信息的有效性；消费者的危险感受、主观危险估计常与商品的客观危险性不一致，所以必须采取一些措施来矫正消费者的危险感受，以确保警示信息发挥应有的安全保障作用     

快速二阶BP网络及其在城市用水量预测中的应用

针对BP网络收敛速度慢，易导致局部极小值的缺点，提出一种快速二阶BP网络，并以城市年用水量预测为例，与BP网络对比，结果表明，该方法加快了收敛速度，提出了结果的准确度。     

地震资料精细处理和解释在徐家围子油田的应用

以地震资料精细处理和解释在大庆长垣外围徐家围子油田开发中的成功应用为例，论述了地震地质综合描述技术在地质情况复杂的低、特低渗透油田开发中的应用效果。     

珠江三角洲土坝和堤围震害初步预测

本项预测采用概率方法,即首先根据广东省水利部门提出的55座土坝和59个重点的座标(见附图),进行概率地震危险性分析,给出它们在一定预测年限T(取50年和100年)的地震烈度Ⅰ的超越概率;其次根据近年我国发生的几次强震时处于不同烈度区内的土坝和堤防的震害资料,形成表示土坝和土堤地震易损性的震害概率矩阵;然后,根据上述两方面结果用概率公式评价各土坝和堤围点在预测年限内各震害等级的超越概率,并取10％超越概率为标准,估计震害等级。结果表明,珠江三角洲土工构筑物的地震危害性南部高于北部。     

地震数据油气预测中的属性优化方法

本文简单介绍了智能信息处理中新出现的RoughSet（RS）理论及属性选择方法，从双相介质地震波传播理论角度，探讨了地震数据油气预测属性优化原理，提出了基于RS理论的地震数据油气预测属性优化方法。实际应用表明；本方法速度快、易实现，而且在优选属性、最大程度地减少提取地震属性种数、提高分类正确率等方面，明显优于其它方法。本方法将成为地震数据油气预测的一种有效手段。     

Measurement and prediction of pressure drop in two-phase flow

Experimental data for air–water two-phase co-current flow in two different pipe diameters were used to test the prediction of pressure drop by a number of existing theories and correlations. Several models are shown to be useful for prediction, particularly with the stratified regimes which have proved difficult to handle in the past. The model suggested by Olujic proved to be of particular value.     

A NEW ALGORITHM FOR ADAPTIVE LATTICE FILTERAND ITS APPLICATION IN THE SPEECH LINEARPREDICTIVE SYNTHESIS

In this paper, an adaptive line spectral pair filter is derived from an adaptive lattice filter. A least-mean-square(LMS) type adaptive algorithm used to calculate directly the line spectral pair(LSP) coefficients on a stage-by-stage basis is presented. Experimental results show that the algorithm has higher convergence rate and lower misadjustment as compared with the other algorithms. The LSP coefficients calculated by the algorithm have been used to carry out speech linear predictive synthesis, resulting in better results than PARCOR coefficients.     

动态模糊神经网络在大坝变形预报中的应用

针对静态模糊神经网络的局限性,提出了在线动态建模的模糊神经网络方法.当新增样本进入训练集之后,根据新样本对模型的贡献大小,在已有模型的基础上进行动态修正,这样可以减少建模的计算时间.新方法实现了增加样本而矩阵阶数不增加,避免了矩阵求逆运算,理论上可以提高计算效率.实例表明动态模糊神经网络方法是可行的,可实现持久预报,具有较强的适应能力和较高的预报精度,可应用于在线实时变形预报及相关领域.     

Network traffic prediction based on a new time series model

Fast and accurate methods for predicting traffic properties and trend are essential for dynamic network resource management and congestion control. With the aim of performing online and feasible prediction of network traffic, this paper proposes a novel time series model, named adaptive autoregressive (AAR). This model is built upon an adaptive memory‐shortening technique and an adaptive‐order selection method originally developed by this study. Compared to the conventional one‐step ahead prediction using traditional Box–Jenkins time series models (e.g. AR, MA, ARMA, ARIMA and ARFIMA), performance results obtained from actual Internet traffic traces have demonstrated that the proposed AAR model is able to support online prediction of dynamic network traffic with reasonable accuracy and relatively low computation complexity. Copyright © 2005 John Wiley & Sons, Ltd.