基于二维导频的OFDM系统信道估计算法研究

提出了基于一种离散傅立叶变换与一维维纳滤波联合的信道估计算法,相比线性内插与一维维纳滤波联合的估计算法仅利用相邻导频获得导频间信道估计,该算法通过插入二维导频对数据分块,利用每个分块的接收数据进行信道估计,从而提高了信道估计的性能.与二维维纳滤波算法相比,该算法在估计性能接近的情况下,计算复杂度大大降低.     

Outlier Detection in Regression Models with ARIMA Errors using Robust Estimates

A diagnostic procedure for detecting additive and innovation outliers as well as level shifts in a regression model with ARIMA errors is introduced. The procedure is based on a robust estimate of the model parameters and on innovation residuals computed by means of robust filtering. A Monte Carlo study shows that, when there is a large proportion of outliers, this procedure is more powerful than the classical methods based on maximum likelihood type estimates and Kalman filtering. Copyright © 2001 John Wiley & Sons, Ltd.     

利用AR模型参数和BP神经网络辨识微震信号

利用AR模型参数和BP神经网络,针对矿山微震信号具有频带较宽、谱成分丰富的特性,提出了时不同频率范围的信号和噪声进行滤波处理的方法.利用该方法可将噪声与信号分离以及将不同频段信号分解,从而达到滤波的目的.实验结果表明,利用AR模型参数和BP神经网络能够有效去除微震异常信号的噪声,可应用于微震信号的预处理和微震预测.     

Estimation and prediction under structural instability: The case of the U.S. pulp and paper market

The objectives of this paper are: first, to show empirically the relevance of using adaptive estimation techniques over more traditional estimation approaches when economic systems are believed to be structurally unstable over time; and secondly, to compare in an empirical framework two adaptive estimation techniques: Kalman filtering and the Carbone–Longini filter. For that purpose, an econometric model for the U.S. pulp and paper market is examined under the assumption of structural instability and, hence, constitutes the basis for comparing forecasting performances and estimation accuracy achieved by each technique. A version of Kalman filtering, modified in line with the basic idea of ‘tracking’ characterizing the Carbone–Longini filter, is also presented and applied. The analysis of the results shows that it may be worth using adapative estimation methods to estimate structurally unstable models, even if there is no prior knowledge about the patterns of variation of the parameters. Also, it shows the Carbone–Longini filter and Kalman filtering as being complementary estimation techniques. An estimation/forecasting methodology involving a sequential application mode of these two techniques is suggested.     

Dynamic mechanistic explanation: computational modeling of circadian rhythms as an exemplar for cognitive science

We consider computational modeling in two fields: chronobiology and cognitive science. In circadian rhythm models, variables generally correspond to properties of parts and operations of the responsible mechanism. A computational model of this complex mechanism is grounded in empirical discoveries and contributes a more refined understanding of the dynamics of its behavior. In cognitive science, on the other hand, computational modelers typically advance de novo proposals for mechanisms to account for behavior. They offer indirect evidence that a proposed mechanism is adequate to produce particular behavioral data, but typically there is no direct empirical evidence for the hypothesized parts and operations. Models in these two fields differ in the extent of their empirical grounding, but they share the goal of achieving dynamic mechanistic explanation. That is, they augment a proposed mechanistic explanation with a computational model that enables exploration of the mechanism’s dynamics. Using exemplars from circadian rhythm research, we extract six specific contributions provided by computational models. We then examine cognitive science models to determine how well they make the same types of contributions. We suggest that the modeling approach used in circadian research may prove useful in cognitive science as researchers develop procedures for experimentally decomposing cognitive mechanisms into parts and operations and begin to understand their nonlinear interactions.     

Modeling of neuronal plasticity by studies of spatio-temporal behavior of neural structures]

In order to point out spatio-temporal filtering of nervous structures after previous studies of their spatial behaviour, we have built a new model of cell according to synaptic and membrane plasticity (it agrees with properties of integration, memory and synaptic fatigue). This model, intended to be produced in many units, is made with integrated circuits in "thick film" technology.     

Reducing uncertainty in short-term projections: Linkage of monthly and quarterly models

This paper shows how monthly data and forecasts can be used in a systematic way to improve the predictive accuracy of a quarterly macroeconometric model. The problem is formulated as a model pooling procedure (equivalent to non-recursive Kalman filtering) where a baseline quarterly model forecast is modified through ‘add-factors’ or ‘constant adjustments’. The procedure ‘automatically’ constructs these adjustments in a covariance-minimizing fashion to reflect the revised expectation of the quarterly model's forecast errors, conditional on the monthly information set. Results obtained using Federal Reserve Board models indicate the potential for significant reduction in forecast error variance through application of these procedures.     

泄漏检测信号滤波技术比较

通过泄漏检测模型试验分析测量信号中的噪声来源，在对比研究传统小波去噪、改进神经网络去噪、最小二乘拟合去噪等方法在实测数据中去噪效果的基础上，借鉴神经网络反向传播学习算法的思路，提出了信号预滤波结合闽值自学习小波去噪的综合滤波方法。该方法通过对恒定状态下带噪压力信号阈值自学习使得重构信号与期望输出均方误差最小来获得单一工况下的最佳去噪阈值，再将此阈值用于同一工况下整个时间段的去噪，这样根据不同工况下得到的最佳阈值可以获得最优输出。数值计算结果比较表明该方法对噪声的抑制作用明显，比传统小波去噪、改进神经网络去噪等方法效果更好。     

蚁群算法中参数设置对超声回波估计性能的影响

摘要针对超声回波参数估计问题存在着耗机时长，估计结果严重依赖于初始值的缺点，本文将蚁群算法应用到超声回波参数估计中，结合超声回波的非线性高斯模型，提出了基于蚁群算法的超声回波参数估计算法，并就蚁群算法在超声回波估计中参数的优化组合设置进行了分析研究通过数值仿真，在信噪比为10dB条件下计算了蚁群算法中各参数的不同取值对估计结果的不同影响，包括计算时间、估计精度和算法稳定性，得出了算法中各参数的组合优化设置，给出了最优参数下的超声回波参数估计结果，并通过与其他算法的比较验证了蚁群算法在超声回波参数估计问题中的有效性．该研究有助于提高超声回波估计的精度和算法的稳定性，缩短蚁群算法的计算时问，以达到优化算法性能的目的．     

Dynamics of vehicle-pavement coupled system based on a revised flexible roller contact tire model

A revised flexible roller contact tire model (RFRC tire model) is proposed,which considers not only the geometric and flexible filtering effect,but also tire damping and pavement displacement.A vehicle-pavement coupled system is modeled as a two DOF oscillator moving along a simply supported beam on a linear viscoelastic foundation.By using the Galerkin's and Direct Integral method,dynamical responses of the vehicle-pavement coupled system are obtained based on the RFRC tire model and the traditional single...     

A dynamic linear model approach for disaggregating time series data

An approach is proposed for obtaining estimates of the basic (disaggregated) series, x_i, when only an aggregate series, y_t, of k period non-overlapping sums of x_i's is available. The approach is based on casting the problem in a dynamic linear model form. Then estimates of x_i can be obtained by application of the Kalman filtering techniques. An ad hoc procedure is introduced for deriving a model form for the unobserved basic series from the observed model of the aggregates. An application of this approach to a set of real data is given.     

基于平均增益模型的连续型(1+1)进化算法计算时间复杂性分析

连续型进化算法的计算时间复杂性分析是进化计算理论研究的一项公开难题,目前相关研究成果较少.针对连续型(1+1)EA,基于适应值差函数提出了平均增益模型及其分析方法,给出了平均计算时间的计算理论,为算法的计算时间复杂性分析提供了依据.在此基础上,研究还选取了学术界关注的球形函数作为研究对象,分别推导了变异步长满足标准正态分布和均匀分布的连续型(1+1)EA在优化球形函数时的平均增益,并估算出了它们的平均计算时间.理论分析说明:1)两种算法的计算时间复杂性都是指数级的;2)在给定相同精度和初始适应值差的前提下,采用均匀分布变异算子的算法其寻优速度优于采用标准正态分布变异算子的算法.进一步地,通过数值实验对理论分析结果进行了验证,结果表明平均增益模型分析是有效的.     

Forecasting load-duration curves

A new method is proposed for forecasting electricity load-duration curves. The approach first forecasts the load curve and then uses the resulting predictive densities to forecast the load-duration curve. A virtue of this procedure is that both load curves and load-duration curves can be predicted using the same model, and confidence intervals can be generated for both predictions. The procedure is applied to the problem of predicting New Zealand electricity consumption. A structural time-series model is used to forecast the load curve based on half-hourly data. The model is tailored to handle effects such as daylight savings, holidays and weekends, as well as trend, annual, weekly and daily cycles. Time-series methods, including Kalman filtering, smoothing and prediction, are used to fit the model and to achieve the desired forecasts of the load-duration curve.     

Selection of Value‐at‐Risk models

Value‐at‐Risk (VaR) is widely used as a tool for measuring the market risk of asset portfolios. However, alternative VaR implementations are known to yield fairly different VaR forecasts. Hence, every use of VaR requires choosing among alternative forecasting models. This paper undertakes two case studies in model selection, for the S&P 500 index and India's NSE‐50 index, at the 95% and 99% levels. We employ a two‐stage model selection procedure. In the first stage we test a class of models for statistical accuracy. If multiple models survive rejection with the tests, we perform a second stage filtering of the surviving models using subjective loss functions. This two‐stage model selection procedure does prove to be useful in choosing a VaR model, while only incompletely addressing the problem. These case studies give us some evidence about the strengths and limitations of present knowledge on estimation and testing for VaR. Copyright © 2003 John Wiley & Sons, Ltd.     

基于FLUENT的NA63A系列翼型气动特性对比分析

利用计算流体力学软件FLUENT6．3．2对在风力发电领域应用较为广泛的NA63A系列翼型的外部流场进行数值模拟，采用Spalar-S．Allmaras湍流计算模型求解该系列翼型在不同攻角下的压力、速度分布。进而对NA63A系列不同翼型在相同攻角下以及网种翼型在不同攻角下的气动特性进行对比分析并总结规律，为该类翼型的性能研究提供了一定的理论依据。     

Forecasting Longevity Gains Using a Seemingly Unrelated Time Series Model

In this paper a multivariate time series model using the seemingly unrelated time series equation (SUTSE) framework is proposed to forecast longevity gains. The proposed model is represented in state space form and uses Kalman filtering to estimate the unobservable components and fixed parameters. We apply the model both to male mortality rates in Portugal and the USA. Our results compare favorably, in terms of mean absolute percentage error, in‐sample and out‐of‐sample, to those obtained by the Lee–Carter method and some of its extensions. Copyright © 2015 John Wiley & Sons, Ltd.     

基于模拟有限差分的嵌入式离散裂缝数学模型

嵌入式离散裂缝模型划分网格时不需要考虑油藏内的裂缝形态,只需对基岩系统进行简单的网格剖分,可以大大降低网格划分的复杂度,从而能够提高计算效率.并且该模型可以将现有成熟的油藏数值模拟技术和离散裂缝网络模型有机地结合起来,能精细地模拟流体在裂缝性油藏中的流动.本文模型求解采用模拟有限差分方法,该方法基于单个网格的节点和面信息构造数值计算格式,理论上适用于任何复杂网格系统,且具有良好的局部守恒性,将其推广到嵌入式离散裂缝模型后,克服了该模型基于有限差分方法求解时不能有效处理全张量形式的渗透率以及不适用于复杂边界形状裂缝性油藏的局限性.最后通过实际算例验证了本文方法的正确性和优越性.     

The filtering of transitory noise in earnings numbers

This paper applies the Kalman filtering procedure to estimate persistent and transitory noise components of accounting earnings. Designating the transitory noise component separately (under a label such as extraordinary items) in financial reports should help users predict future earnings. If a firm has no foreknowledge of future earnings, managers can apply a filter to a firm's accounting earnings more efficiently than an interested user. If management has foreknowledge of earnings, application of a filtering algorithm can result in smoothed variables that convey information otherwise not available to users. Application of a filtering algorithm to a sample of firms revealed that a substantial number of firms exhibited a significant transitory noise component of earnings. Also, for those firms whose earnings exhibited a significant departure from the random walk process, the paper shows that filtering can be fruitfully applied to improve predictive ability.     

梯级水电站群长期发电优化调度多核并行随机动态规划方法

随机动态规划求解水电站群长期发电优化调度易产生"维数灾"问题,导致计算耗时急剧增加,求解效率降低.如何缓解维数灾和提高计算效率,一直是水库优化调度致力于研究的难点问题.在随机动态规划的并行性分析基础上,提出了基于Fork/Join并行框架的多核并行随机动态规划方法.该方法将单个时段内所有变量组合状态下的计算任务作为父任务,通过分治法递归分解为多个子任务,并平均分配到不同的内核同时计算实现细粒度并行求解.以澜沧江下游梯级水电站群为研究实例,建立了3个变量离散数不同的调度方案,并在多核环境下验证该方法的计算效率.结果表明,在2和4核环境下,该方法的计算耗时与串行方法相比,分别节省了约50%和70%,大幅度缩减计算耗时,可充分利用多核资源;同时,计算任务的规模越大,并行计算的耗时缩减幅度越大.因此,此方法为大规模水电系统优化调度提供了一种可行途径,其并行原理可为其他应用所借鉴.     

On the forecasting of high-frequency financial time series based on ARIMA model improved by deep learning

Through empirical research, it is found that the traditional autoregressive integrated moving average (ARIMA) model has a large deviation for the forecasting of high-frequency financial time series. With the improvement in storage capacity and computing power of high-frequency financial time series, this paper combines the traditional ARIMA model with the deep learning model to forecast high-frequency financial time series. It not only preserves the theoretical basis of the traditional model and characterizes the linear relationship, but also can characterize the nonlinear relationship of the error term according to the deep learning model. The empirical study of Monte Carlo numerical simulation and CSI 300 index in China show that, compared with ARIMA, support vector machine (SVM), long short-term memory (LSTM) and ARIMA-SVM models, the improved ARIMA model based on LSTM not only improves the forecasting accuracy of the single ARIMA model in both fitting and forecasting, but also reduces the computational complexity of only a single deep learning model. The improved ARIMA model based on deep learning not only enriches the models for the forecasting of time series, but also provides effective tools for high-frequency strategy design to reduce the investment risks of stock index.