基于灰色理论的设备状态预测

针对一般GM(1,1)多步预测方法的不足,提出了一种基于代谢递补GM(1,1)的状态预测方法.该方法首先对原始信号进行预处理,再通过递补思想进行多步预测,然后利用更新数据进行代谢预测,最后计算设备状态的预测值与真实值误差,根据预测值的趋势判断设备的未来状态.实例分析结果表明,该方法所需数据样本少且数据训练时间短,后八步的预测精度可满足工程精度,能有效地应用于设备的中长期在线状态预测.     

混流式水轮机内部流动的整体解析及气蚀预测

采用计算流体力学方法,将整个水轮机划分为蜗壳、固定导叶叶栅、活动导叶叶栅、转轮和尾水管等几个域,对混流式水轮机进行整体解析,并根据各个域的残差履历,以转轮残差的充分收敛作为迭代计算是否收敛的判据.最后以某水电厂的混流式水轮机作为研究对象,得到了混流式水轮机的静态压力场,并利用压力场预测了转轮叶片出口负压面气蚀的发生时刻及发生部位,所得结果与实际转轮出口负压面气蚀结果一致,表明了文中方法的有效性.     

导电高分子复合材料逾渗阈值的预测

逾渗阈值是表征导电高分子复合材料导电性能的重要参数之一,但是由于影响因素异常复杂,逾渗阈值的预测存在不确定性.文中从导电复合材料的微观角度出发,分析了导电高分子复合材料的逾渗行为及机理,并基于Flory的凝胶化理论和Bueche的无限网链模型,提出了一种新的预测导电高分子复合材料逾渗阈值φc的方法,最后将理论预测值与其它研究成果进行了比较分析.结果表明:由此方法计算得到的逾渗阈值更加接近高密度聚乙烯/碳黑导电复合材料的实验结果.     

叶片式混输泵气液两相流及性能的数值分析

利用Fluent计算软件在多重参考坐标系下采用欧拉方法的双流体湍流模型计算螺旋轴流式叶片泵内高含气状态下的三维气液两相流场.通过对泵内绝对流速、叶轮相对流速、静态压力、气液两相分布及其相间滑移速度矢量的分析,探讨了气液两相介质在泵内的流动规律.结果显示离心力的作用使叶轮内液相主要在轮缘附近流动,而气相则聚集在轮毂附近;泵导叶内气液两相分离状况有较明显改善.通过与泵性能实验结果对比,验证了文中方法对气液两相叶片式混输泵计算分析的有效性.     

基于模型预测的网络化控制系统补偿控制

针对分时控制模式下存在于网络诱发延迟大于一个采样周期的网络化随机控制系统中的空采样和数据拒绝问题,提出了一种基于模型预测的补偿控制法。该方法通过在执行器设立缓冲区窗口并存放一定数目的历史控制量,利用多个历史控制量对当前采样周期的控制状态进行估计预测,对空采样和数据拒绝情况进行补偿控制,达到改善系统性能的目的。通过仿真实验证明了该补偿控制算法的有效性。     

我国6大境外客源市场旅游本底趋势线的建立

提出了旅游本底趋势线的概念,并依据1984年～1996年客流量统计数据,建立了日本、美国、英国、德国、法国和澳大利亚6大境外市场来华旅游的本底趋势线;利用本底线延伸的预测功能,预测了6大境外市场未来4年的变化趋势.     

非线性离散动态大系统的DISOPE关联预测递阶算法

提出了一种非线性离散动态大系统系统优化与参数估计集成的关联预测递阶算法 .在各子系统模型与实际存在差异的情况下 ,将动态系统系统优化与参数估计集成 ( DISOPE)方法与关联预测法相结合 ,得到一个上级协调 ,下级进行各子系统优化与参数估计集成的双环迭代算法 ,从模型出发通过迭代运算能得到实际系统在存在模型 -实际差异时的真实最优解 .仿真结果表明了算法的有效性和实用性.     

An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting

For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive multiscale ensemble learning paradigm incorporating ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO) and least square support vector machines (LSSVM) with kernel function prototype is developed. Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine‐to‐coarse reconstruction algorithm, the high‐frequency, low‐frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high‐frequency components. LSSVM is suitable for forecasting the low‐frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. Copyright © 2016 John Wiley & Sons, Ltd.     

Dynamic Latent Class Model Averaging for Online Prediction

We consider the problem of online prediction when it is uncertain what the best prediction model to use is. We develop a method called dynamic latent class model averaging, which combines a state‐space model for the parameters of each of the candidate models of the system with a Markov chain model for the best model. We propose a polychotomous regression model for the transition weights to assume that the probability of a change in time depends on the past through the values of the most recent time periods and spatial correlation among the regions. The evolution of the parameters in each submodel is defined by exponential forgetting. This structure allows the ‘correct’ model to vary over both time and regions. In contrast to existing methods, the proposed model naturally incorporates clustering and prediction analysis in a single unified framework. We develop an efficient Gibbs algorithm for computation, and we demonstrate the value of our framework on simulated experiments and on a real‐world problem: forecasting IBM's corporate revenue. Copyright © 2014 John Wiley & Sons, Ltd.     

A Time‐Simultaneous Prediction Box for a Multivariate Time Series

A sample‐based method in Kolsrud (Journal of Forecasting 2007; 26 (3): 171–188) for the construction of a time‐simultaneous prediction band for a univariate time series is extended to produce a variable‐ and time‐simultaneous prediction box for a multivariate time series. A measure of distance based on the L_∞ ‐norm is applied to a learning sample of multivariate time trajectories, which can be mean‐ and/or variance‐nonstationary. Based on the ranking of distances to the centre of the sample, a subsample of the most central multivariate trajectories is selected. A prediction box is constructed by circumscribing the subsample with a hyperrectangle. The fraction of central trajectories selected into the subsample can be calibrated by bootstrap such that the expected coverage of the box equals a prescribed nominal level. The method is related to the concept of data depth, and thence modified to increase coverage. Applications to simulated and empirical data illustrate the method, which is also compared to several other methods in the literature adapted to the multivariate setting. Copyright © 2015 John Wiley & Sons, Ltd.