风电场可靠性模型及其应用

考虑了风速分布的季节性差异和风速的随机变化的影响、风电机组的实际功率特性、风电场尾流效应以及风电机组故障状况,建立了风电场可靠性模型.根据某海上风电场实测数据进行数值仿真实验,结果验证了模型的正确性和有效性.模型为进一步研究大规模风电并网以及风电场功率预测等问题打下了基础.     

人口增长率的非参数自回归预测模型

针对传统的人口增长预测模型不能理想地捕获我国人口增长率数据的非线性性特征,本文基于局部线性非参数估计理论,对我国建国以来的年人口增长率建立了非参数自回归NAR(1)模型,并对2000-2003年的年人口增长率进行了预测,计算结果表明,相对于参数自回归模型而言,非参数自回归模型能够很好地解决人口增长预测这一非线性问题,预测精度较高。     

非线性自回归模型的几何非常返性

本文研究了非线性自回归模型的随机稳定性.通过建立恰当的Foster-Lyapunov条件,得到了非线性自回归模型几何非常返的充分条件.     

基于SVR的RAR区域经济预测模型

由于区域经济系统中许多经济变量呈现出强非线性与大波动性的特征,使得传统的时间序列线性建模和预测技术难以适应区域经济预测的要求.为此,提出基于支持向量机改进的残差自回归区域经济预测模型.首先采用时间序列分析中的残差自回归模型对时间序列趋势进行线性拟合,然后对残差自回归模型估计后的残差序列采用支持向量回归方法再次提取其非线性特征,从而提高区域经济时间序列模型的预测精度.最后以广东省GDP的预测实例说明模型的有效性.     

基于门限自回归模型下物价指数时间序列分析

对于非线性、非稳定的时间序列, 门限自回归模型具有较好的预测效果. 本文根据四川省1952--2005年商品零售物价指数的资料, 运用门限自回归分析方法对四川省近五十年来的商品零售物价指数进行了时间序列分析, 得到的模型拟合效果较好, 并适合于短期预测, 从而为政府管理物价提供了较精确的数量依据.     

Grid-Search和PSO优化的SVM在Shibor回归预测中的应用研究

作为一种动态和非稳定时间序列,Shibor发展变化是随机波动的,难以准确预测Shibor的波动性.支持向量机(SVM)在回归预测非线性时间序列方面有很好地预测效果,SVM的预测精度和泛化能力的核心是参数的优化选择,分别用网格搜索法(Grid-Search)和粒子群(PSO)算法来优化SVM的参数c和g.从而将参数优化后的SVM非线性回归预测法与基于传统ARIMA时间序列预测结果进行对比分析.实验表明,优化后的SVM回归预测方法比ARIMA时间序列方法更精确,在实际中具有很大的应用价值.     

非线性自回归模型的几何非常返性（英文）

本文研究了非线性自回归模型的随机稳定性.通过建立恰当的Foster-Lyapunov条件,得到了非线性自回归模型几何非常返的充分条件.     

基于非参数自回归模型的黄金价格短期分析预测

黄金价格的变化受到众多因素的影响,总体呈波动上涨趋势。为了拟合我国黄金现货价格的非线性趋势,本文基于局部线性估计理论,建立了我国黄金现货价格的非参数自回归预测模型NAR(P)。文中结果表明,NAR(P)模型能够较好的拟合黄金价格的非线性趋势,预测误差较小。该模型具有较高的应用价值。     

半参数平滑转换分位数自回归模型及应用

已有针对平滑转换自回归模型(STAR)的研究多是将转换函数设定为Logistic函数或指数函数形式,并在均值回归框架下获得模型的估计、检验及预测结果.文章基于重心权有理插值和分位数回归方法,构建一类新的半参数平滑转换分位数自回归模型,其主要特点表现在:第一,基于重心权有理插值方法构造的平滑转换函数,形式更加灵活自由,有效减少了模型误设的风险.第二,在分位数回归框架下,利用遗传算法获得新模型在不同分位点处的平滑转换自回归系数估计,比单纯的均值回归得到的信息更为丰富.数值模拟结果显示,新模型的平滑转换自回归系数估计在无偏性、有效性和一致性方面均具有较好表现.最后,将新模型应用于上证综指日收益率的动态趋势及预测研究,细致揭示了收益率序列在不同阶段、不同分位点处的非线性和异质性变化特征.     

基于改进的支持向量回归机的金融时序预测

金融市场是一个复杂、演化、非线性的动态变化的系统.金融数据往往带有噪声,非平稳且时常是混沌的.本文基于时序数据的先验知识——近期数据对于预测未来走势提供了更多的信息,对于传统的支持向量机的回归模型做出了一定的改进,即对于近期的数据预测错误施以更严重的惩罚,构建了改进的支持向量回归机模型.使用该改进模型对中国股票市场指数时间序列进行了预测,结果显示,本文改进的模型较之传统的支持向量回归机模型和神经网络模型有较好的预测效果.     

A semiparametric method for estimating nonlinear autoregressive model with dependent errors

The first-order nonlinear autoregressive model is considered and a semiparametric method is proposed to estimate regression function. In the presented model, dependent errors are defined as first-order autoregressive AR(1). The conditional least squares method is used for parametric estimation and the nonparametric kernel approach is applied to estimate regression adjustment. In this case, some asymptotic behaviors and simulated results for the semiparametric method are presented. Furthermore, the method is applied for the financial data in Iran’s Tejarat-Bank.     

On filtering and estimation of a threshold stochastic volatility model

We derive a nonlinear filter and the corresponding filter-based estimates for a threshold autoregressive stochastic volatility (TARSV) model. Using the technique of a reference probability measure, we derive a nonlinear filter for the hidden volatility and related quantities. The filter-based estimates for the unknown parameters are then obtained from the EM algorithm.     

柔长结构气固耦合的线性与非线性气动力理论

针对柔性结构与风在三方向相互作用的特点,在合理的结构节段力学模型的基础上,建立了新的气动力模型,即三分力系数C_i=C_i(β(t),θ),(#em/em#=D,L,M)不仅是瞬时攻角的函数,而且也是转速的函数,并依据“片条理论”与改进的“准静态理论”,提出了推导结构节段模型与风相互作用的线性与非线性气动力项的方法,从而将土木工程中柔性结构与风的相互作用的线性与非线性理论集中到一个模型中.对于线性气动力部分,给出了与经典气动力公式中相对应的颤振导数的半解析表达式.对于非线性气动力部分,给出了扭转气动耦合的非线性气动力表达式,并给出了Tacoma大桥扭转非线性运动的控制方程,其形式与结果与V.F.B-m的相吻合.     

A Hessian Regularized Nonlinear Time Series Model

We introduce a nonparametric nonlinear time series model. The novel idea is to fit a model via penalization, where the penalty term is an unbiased estimator of the integrated Hessian of the underlying function. The underlying model assumption is very general: it has Hessian almost everywhere in its domain. Numerical experiments demonstrate that our model has better predictive power: if the underlying model complies with an existing parametric/semiparametric form (e.g., a threshold autoregressive model (TAR), an additive autoregressive model (AAR), or a functional coefficient autoregressive model (FAR)), our model performs comparably; if the underlying model does not comply with any preexisting form, our model outperforms in nearly all simulations. We name our model a Hessian regularized nonlinear model for time series (HRM). We conjecture on theoretical properties and use simulations to verify. Our method can be viewed as a way to generalize splines to high dimensions (when the number of variates is more than three), under which an analogous analytical derivation cannot work due to the curse of dimensionality. Supplemental materials are provided, and will help readers reproduce all results in the article.     

Aeroelastic analysis of large horizontal wind turbine baldes?

A nonlinear aeroelastic analysis method for large horizontal wind turbines is described. A vortex wake method and a nonlinear ?nite element method (FEM) are coupled in the approach. The vortex wake method is used to predict wind turbine aero-dynamic loads of a wind turbine, and a three-dimensional (3D) shell model is built for the rotor. Average aerodynamic forces along the azimuth are applied to the structural model, and the nonlinear static aeroelastic behaviors are computed. The wind rotor modes are obtained at the static aeroelastic status by linearizing the coupled equations. The static aeroelastic performance and dynamic aeroelastic responses are calculated for the NH1500 wind turbine. The results show that structural geometrical nonlinearities signi?cantly reduce displacements and vibration amplitudes of the wind turbine blades. Therefore, structural geometrical nonlinearities cannot be neglected both in the static aeroelastic analysis and dynamic aeroelastic analysis.     

Model selection of a switching mechanism for financial time series

The threshold autoregressive model with generalized autoregressive conditionally heteroskedastic (GARCH) specification is a popular nonlinear model that captures the well‐known asymmetric phenomena in financial market data. The switching mechanisms of hysteretic autoregressive GARCH models are different from threshold autoregressive model with GARCH as regime switching may be delayed when the hysteresis variable lies in a hysteresis zone. This paper conducts a Bayesian model comparison among competing models by designing an adaptive Markov chain Monte Carlo sampling scheme. We illustrate the performance of three kinds of criteria by comparing models with fat‐tailed and/or skewed errors: deviance information criteria, Bayesian predictive information, and an asymptotic version of Bayesian predictive information. A simulation study highlights the properties of the three Bayesian criteria and the accuracy as well as their favorable performance as model selection tools. We demonstrate the proposed method in an empirical study of 12 international stock markets, providing evidence to strongly support for both models with skew fat‐tailed innovations. Copyright © 2016 John Wiley & Sons, Ltd.     

A Combinatorial Approach to Piecewise Linear Time Series Analysis

Over recent years, several nonlinear time series models have been proposed in the literature. One model that has found a large number of successful applications is the threshold autoregressive model (TAR). The TAR model is a piecewise linear process whose central idea is to change the parameters of a linear autoregressive model according to the value of an observable variable, called the threshold variable. If this variable is a lagged value of the time series, the model is called a self-exciting threshold autoregressive (SETAR) model. In this article, we propose a heuristic to estimate a more general SETAR model, where the thresholds are multivariate. We formulate the task of finding multivariate thresholds as a combinatorial optimization problem. We develop an algorithm based on a greedy randomized adaptive search procedure (GRASP) to solve the problem. GRASP is an iterative randomized sampling technique that has been shown to quickly produce good quality solutions for a wide variety of optimization problems. The proposed model performs well on both simulated and real data.     

大型水平轴风力机叶片气动弹性计算

给出了一种考虑几何非线性的大型风力机静、 动气动弹性一体化计算方法．采用涡尾迹方法进行风力机气动载荷计算．建立风力机风轮的三维壳模型．沿周向平均风力机叶片载荷并加载到结构模型进行非线性静气动弹性分析．基于动力学小扰动假设, 在静平衡构型下进行动力学线性化, 计算风轮固有振动特性．继而结合非定常涡尾迹方法计算风力机动气动弹性响应．计算了NH 1500叶片考虑几何非线性的静气动弹性位移和动气动弹性响应．结果表明,大型风力机叶片几何非线性较为明显地减小静气动弹性位移,同时降低动气动弹性的响应幅值．大型风力机气动弹性响应计算需要考虑几何非线性     

THE PROBABILISTIC PROPERTIES OF THE NONLINEAR AUTOREGRESSIVE MODEL WITH CONDITIONAL HETEROSKEDASTICITY

1.IntroductionTakeseriesmodelswithconditionalheteroskedasticityarewidelyappliedinmanyareassuchasfinancialeconomicsandeconometrics.Consequently,thereisagrowingthetheoreticalinterestinthesemodels.Inthispaper,weareexclusivelyinterestedinthefollowingnonlinearautoregressivemodelwithconditionalheteroskedasticityat=rk(Xt~1,...Iac~p,01) eth(Xt~1,'IXo~~l02)(1.1)withthefollowingassumptions(i){Et}isasequenceofindependelltideticallydistributedrandomvariableswithacommonalmenteveryWherepositivedenistyi…     

A low-order active fault-tolerant state space self-tuner for the unknown sampled-data nonlinear singular system using OKID and modified ARMAX model-based system identification

In this paper, we present two control schemes for the unknown sampled-data nonlinear singular system. One is an observer-based digital redesign tracker with the state-feedback gain and the feed-forward gain based on off-line observer/Kalman filter identification (OKID) method. The presented control scheme is able to make the unknown sampled-data nonlinear singular system to well track the desired reference signal. The other is an active fault tolerance state-space self-tuner using the OKID method and modified autoregressive moving average with exogenous inputs (ARMAX) model-based system identification for unknown sampled-data nonlinear singular system with input faults. First, one can apply the off-line OKID method to determine the appropriate (low-) order of the unknown system order and good initial parameters of the modified ARMAX model to improve the convergent speed of recursive extended-least-squares (RELS) method. Then, based on modified ARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown sampled-data nonlinear singular system with immeasurable system state. Moreover, in order to overcome the interference of input fault, one can use a fault-tolerant control scheme for unknown sampled-data nonlinear singular system by modifying the conventional self-tuner control (STC). The presented method can effectively cope with partially abrupt and/or gradual system input faults. Finally, some illustrative examples including a real circuit system are given to demonstrate the effectiveness of the presented design methodologies.