基于云理论的径流不确定性推理模型研究

针对中长期径流预报中存在许多不确定性因素,本文引入云理论构建径流预报的不确定性推理模型（UR）.首先,该模型应用最大方差方法（MaxVar）对径流序列进行硬性分级,用级别概念表示径流分级区间,以期望（Ex）、熵（En）以及超熵（He）构成的云隶属函数描述径流级别概念的模糊性和随机性,实现分级区间软化,然后将径流量值进行属性转化,以此建立定性推理规则集,运用云算法进行径流不确定推理预报,成功实现径流序列不确定性传递;其次,对径流分级过程中超熵（He）参数确定进行了初探,对推理随机性输出结果进行统计分析,给出相应显著水平下的预报区间;最后,将该模型应用于南方某水库入库月径流预报中,并与广泛应用的最小二乘支持向量机（LSSVM）和ARMA模型进行比较分析,本文模型不仅具有较高的预报精度,而且能够进行区间预报,实例验证说明了模型的有效性和实用性.     

多状态网络可靠性与韧性评估方法综述

为了准确评估复杂系统的可靠性,通常使用网络拓扑结构模型研究系统在各种情况下稳定运行的能力.但现实世界中的许多网络不仅存在连通性要求,还需要传输一定的流量以保证网络具有一定的吞吐量,该网络被称为多状态网络.多状态网络可靠性模型广泛应用于现实中的网络系统,如制造、能源、交通、信息、物流和装备保障、指挥控制和无人集群等.然而,不断增长的系统规模与复杂程度,导致求解多状态网络可靠度愈发困难.因此,寻求更加高效的方法来求解网络的可靠度成为迫切需要解决的难点问题.本文主要对多状态网络可靠性求解算法进行综述,总结了近年来其效率改善方面的研究进展.通过有效降低多状态网络可靠性的评估复杂度,大幅提高该方法的算法效率和可求解网络的规模,为管理者在多状态网络的设计、建造、运行和维护过程中提供支撑,确保多状态网络的稳定、可靠运行.韧性是可靠性的延伸,是衡量系统对抗扰动并从中恢复的能力,能够反映系统毁伤及恢复的全过程.本文进一步讨论了多状态网络韧性评估方法及其进展.     

基于贝叶斯网络的变压器故障诊断及重要度分析

以云南电网公司提供的500 kV变压器历史运行数据为依据,利用贝叶斯网络和数据挖掘技术,结合变压器油中溶解气体分析的改良三比值法,针对500 kV变压器建立故障诊断模型,并对引起变压器故障的各影响因素进行重要度分析。最后,利用变压器实时运行数据进行试验,验证了模型的有效性和实用性。     

基于贝叶斯网络的武器装备体系脆性分析

武器装备体系的脆性是其固有特性,直接关系到武器装备体系作战效能的稳定发挥.武器装备体系的脆性分析主要研究体系的子系统在遭受内外部干扰或破坏后,体系出现整体损伤或崩溃的现象,并分析导致体系崩溃的关键因素.本文利用贝叶斯网络对体系内部子系统之间复杂的相互作用和因果关系进行建模,从而将体系的脆性分析问题转化为体系崩溃的后验概率计算问题,进而可通过贝叶斯网络的信念传播算法和MCMC随机采样来有效计算.本文还提出将体系内部的正反馈回路建模为动态贝叶斯网络,从而可通过网络的概率推断,动态分析体系脆性的内部传导机制.本文提出的脆性分析方法可为武器装备体系的设计与定量分析提供一种有效的技术手段.     

网络态势感知系统的告警阈值确定方法研究

在网络态势感知系统中需要对影响网络性能的各项指标设置阈值,通过设置阚值和阚值检查可以在网络出现性能问题时及时向管理人员告警.本文提出了一种利用BP神经网络来确定告警阚值的方法:在采集到的大量性能数据中选取典型值作为训练样本训练BP神经网络.输出该值隶属于各模糊区问的隶属度,最后利用检验样本找到各区间的分界点即为阈值.文章还利用MATLAB对BP神经网络进行了仿真实验,验证了该方法的有效性.     

基于Lyapunov-Elman的网络控制系统时延预测方法

网络控制系统中的时延是影响系统性能的重要参数,针对基于Internet的网络控制系统中时延预测问题,提出一种最大Lyapunov指数与Elman神经网络结合的预测方法.首先对时延序列进行相空间重构,得到嵌入维数与延迟变量,然后通过最大Lyapunov指数方法与Elman神经网络对时延分别进行一步预测,将两种预测方法的预测结果通过不同的权值系数进行叠加得到最终的时延预测值.最后针对权值系数的寻优问题,提出一种改进的自由搜索算法,其收敛精度与速度都优于标准的自由搜索算法.仿真实验表明,相对于其它预测方法,本文的基于Lyapunov-Elman的时延预测方法具有较高的预测精度与较小的预测误差.     

基于异构数据模型的电-气耦合网络状态感知方法研究

随着电力、天然气网络开放互联,多能流耦合在实现能源科学管理与优化调度的同时,彼此间能量转换使得原本独立能源网络的状态感知复杂度增加.因此,在实现快速分析及考虑耦合网络特性的基础上,本文提出一种针对多维电-气耦合网络的异构数据模型状态感知方法.首先,针对子网络交互影响以及相应检测变量变化过程,构建出刻画不同子网络时间响应...     

Inflation forecasting for aggregates of the EU-7 and EU-14 with Bayesian VAR models

This article applies the Bayesian Vector Auto-Regressive (BVAR) model to key economic aggregates of the EU-7, consisting of the former narrow-band ERM members plus Austria, and the EU-14. This model appears to be useful as an additional forecasting tool besides structural macroeconomic models, as is shown both by absolute forecasting performance and by a comparison of ex-post BVAR forecasts with forecasts by the OECD. A comparison of the aggregate models to single-country models reveals that pooling has a strong impact on forecast errors. If forecast errors are interpreted as shocks, shocks appear to be—at least in part—asymmetric, or countries react differently to shocks. © 1998 John Wiley & Sons, Ltd.     

A state space approach to forecasting the final vintage of revised data with an application to the index of industrial production

There is considerable interest in the index of industrial production (IIP) as an indicator of the state of the UK's industrial base and, more generally, as a leading economic indicator. However, this index, in common with a number of key macroeconomic time series, is subject to revision as more information becomes available. This raises the problem of forecasting the final vintage of data on IIP. We construct a state space model to solve this problem which incorporates bias adjustments, a model of the measurement error process, and a dynamic model for the final vintage of IIP. Application of the Kalman filter produces an optimal forecast of the final vintage of data.     

Performance evaluation of neural network architectures: the case of predicting foreign exchange correlations

In the last decade, neural networks have emerged from an esoteric instrument in academic research to a rather common tool assisting auditors, investors, portfolio managers and investment advisors in making critical financial decisions. It is apparent that a better understanding of the network's performance and limitations would help both researchers and practitioners in analysing real‐world problems. Unlike many existing studies which focus on a single type of network architecture, this study evaluates and compares the performance of models based on two competing neural network architectures, the multi‐layered feedforward neural network (MLFN) and general regression neural network (GRNN). Our empirical evaluation measures the network models' strength on the prediction of currency exchange correlation with respect to a variety of statistical tests including RMSE, MAE, U statistic, Theil's decomposition test, Henriksson–Merton market timing test and Fair–Shiller informational content test. Results of experiments suggest that the selection of proper architectural design may contribute directly to the success in neural network forecasting. In addition, market timing tests indicate that both MLFN and GRNN models have economically significant values in predicting the exchange rate correlation. On the other hand, informational content tests discover that the neural network models based on different architectures capture useful information not found in each other and the information sets captured by the two network designs are independent of one another. An auxiliary experiment is developed and confirms the possible synergetic effect from combining forecasts made by the two different network architectures and from incorporating information from an implied correlation model into the neural network forecasts. Implied correlation and random walk models are also included in our empirical experiment for benchmark comparison. Copyright © 2005 John Wiley & Sons, Ltd.