上次对角双线性时间序列模型的广义传递函数系统及平稳性条件

本文讨论了一类特殊的双线性时间序列模型,即上次对角欢线性时间序列模型,在输入为严格白噪声的假定下,利用多元Wiener-Ito积分,得到了该模型的广义传递函数及平稳性的充分必要条件。     

增长型经济变量的趋势时间序列预测模型

针对增长型外汇储备时间序列变化复杂性的特点,可以建立确定性趋势的时间序列模型及包含单位根的随机趋势模型.实际计算显示,确定性趋势的时间序列模型具有较高的预测精度.     

青岛港货运吞吐量的时间序列模型

运用时间序列分析方法对时间序列建立ARMA,ARIMA模型.搜集了青岛港1999年1月～2003年5月的货运吞吐量数据,对进行分析,建立了青岛港货运吞吐量的模型.通过预留的部分数据对模型进行检验,并对模型的残差进行检验,得出模型比较合理.     

小波的传递函数构造法

本文从小波与尺度函数的传递函数出发 ,给出了构造小波母函数及尺度函数的构造方法 .根据此方法 ,首先以小波与其尺度函数的传递函数为起点 ,构造了一个非正交小波 ,随后以此小波和一个已有的非正交小波为基准 ,进一步推广得到了一类非正交小波及尺度函数类 .在非正交小波的基础上 ,利用将尺度函数正交化的方法 ,构造出了相应正交小波的函数族 .     

ARIMA乘积季节模型及其在传染病发病预测中的应用

本文研究乘积季节模型在传染病发病情况预测中的应用,并探讨提高模型准确性和实用性的途径.以1980年1月至2000年7月江苏省肾综合征出血热发病资料建立模型,以2000年的发病资料作为模型预测效果的考核样本.首先采用差分方法对序列资料进行平稳化,然后进行定阶并估计参数,建立乘积季节模型,最后对预测结果进行检验和分析.从而更好地掌握未来疫情动态发展趋势.检验结果表明,用乘积季节模型对肾综合征出血热月发病情况的拟合结果满意,预测效果良好.     

基于时间序列的多因素耦合下GDP预测研究——以伊犁州为例

以时间序列分析相关理论为基础,首先介绍了ARIMA模型和VAR模型的建模过程,然后构造ARIMA模型对伊犁州直GDP进行预测,再考虑进出口总额、社会消费品零售额、工业和建筑业、社会固定资产总额等因素的耦合效应,构造VAR模型对伊犁州直GDP进行预测,最后对两种方法做对比分析,结果显示,VAR模型预测精度较高,误差较小,经济含义明确,预测的GDP结果可为伊犁州党委、政府制定相关经济政策和发展战略提供一定的科学依据.     

我国人口时间序列拟合模型的比较与选择

通过对我国1990年至2007年共18年的人口数据进行实证分析,运用时间序列的三个不同模型,对我国人口的变化规律进行了拟合研究;并给出了反映各个模型拟合精度的AIC值和SBC值;最后,通过对所建模型的比较分析,对拟合模型的选择提出了相关建议.     

基于时间序列分析方法的伊犁州GDP的预测研究

基于时间序列理论,以伊犁州1978年至2014年来生产总值为基础数据,利用Eviewes8.0软件对数据进行处理分析,并对模型进行显著性检验,综合各种条件确定最终时间序列回归模型,对伊犁州未来三年的生产总值做出预测,为伊犁州党委、政府制定相关经济政策和发展战略提供科学依据.     

烟台地区降水量的AR IMA随机模型研究

采用自回归求积移动平均法(AR IM A),对烟台地区历年来的降水量动态数据进行了分析.结果显示,AR IM A(3,1,2)模型提供了较准确的预测效果,相对误差变化在0.21%~5.75%,可以用于未来的预测,并为烟台市降水量的预测提供了可靠依据.     

广西GDP的统计预测模型及其应用

本文利用SPSS统计软件及非参数统计方法(卡方检验和K-S检验法)对广西1950年至2006年共57年的GDP数据进行实证分析.在最佳准则(即AIC准则)下建立了ARIMA(1,2,1)时间序列模型,并利用非参数统计方法对此模型进行了适应性检验,然后利用2001年至2006年的实际值与该模型的预测值进行了比较.最后,本文利用该模型对广西未来五年的GDP进行了预测.     

n-Order linear state space systems: Computing the transfer function using the DFT

The discrete Fourier transform is used to determine the coefficients of a transfer function for n-order linear systems: . The algorithm is fast, straight forward and easily can be implemented. Two step-by-step examples, illustrating the application of the algorithm, are presented.     

基于ARIMA模型对海南省社会消费品零售总额的预测

对海南省社会消费品零售总额进行预测,对于了解海南省社会消费品零售总额的发展态势,为有关部门作出决策提供科学的依据,具有重大的现实经济意义.选取1999年到2014年的海南省社会消费品零售总额的数据来建立ARIMA(1,3,2)模型,2012年到2014年的实际值与预测值的相对误差5%以内,拟合效果良好,说明采用ARIMA模型预测海南省社会消费品零售总额是可行的,预测数据可靠.最后对海南省2015-2018年的社会消费品零售总额进行预测.     

Complex dynamic behaviour of an economic cycle model

In this paper, we investigate the dynamics of a nonlinear economic cycle model. The necessary and sufficient conditions are given to guarantee the existence and stability of the fixed point. It is also shown that the system undergoes a Neimark–Sacker bifurcation by using center manifold theorem and bifurcation theory. Furthermore, Marotto’s chaos is proved when certain conditions are satisfied. Numerical simulations are presented not only to illustrate our results with the theoretical analysis, but also to exhibit the complex dynamical behaviour, such as the period-10, -16, -20 orbits, attracting invariant cycles, quasi-periodic orbits, 10-coexisting chaotic attractors, and boundary crisis. Specifically, we have stabilized the chaotic orbits at an unstable fixed point using the feedback control method.     

A transfer function model to describe odor causing VOCs transport in a ventilated airspace with mixing/adsorption heterogeneity

The ability of a transfer function modeling technique is evaluated to explain the odor causing VOCs (VOC-odor) transport processes influenced by heterogeneity of adsorption surface of ambient aerosol and air mixing pattern in a ventilated airspace. An advection–reaction impulse/step response function is used to generalize the dynamic transport of VOC-odor in heterogeneous mixing/adsorption ventilated airspace. The system process presented by an ensemble transfer function is solved analytically in the Laplace domain. The model requires the specification of probability density function (pdf) for residence time of airflow and for both equilibrium linear partitioning and first-order mass transfer rate parameters of gas/solid phase to quantify the specific air mixing pattern and transport processes. The model predicts the ensemble mean VOC-odor concentrations for a variety of adsorption kinetics and mixing pattern combinations as a function of the boundary impulse/step response inputs as well as residence time and adsorption rate statistics. The general behavior of output VOC-odor profiles is analyzed through the effects of mean adsorption rate coefficient, mean linear partitioning constant, mixing efficiency, mean residence time and coefficient of variations of both linear partitioning and rate coefficients. This study indicates that when mixing/adsorption heterogeneity exists, simple complete mixing assumption and simple distribution of rate constant are inherently not sufficient to represent a more generally distributed mixing/adsorption process of VOC-odor transport in a ventilated airspace.     

A model for speculation in a dynamic economy

The paper deals with speculation strategies in a dynamic economy, where “speculation” means participating in a market with the intention to gain a reward by first buying an item and thereafter selling it at a possibly higher price. By assuming that the states of the economy form a Markov chain the problem is modeled as a discrete time Markov decision process. The optimal strategies (which are pairs of stopping times) are identified. Under quite general conditions the optimal rule for the selling process turns out to be a control limit policy in both state of economy and time. Techniques for the computation of optimal strategies are presented; some numerical examples are also discussed. For a static economy closed-form solutions are given     

动态特性稳健设计中的响应曲面分析

针对含有单个噪声因素的动态特性的稳健设计，建立了响应曲面模型，给出了模型的统计分析方法，得到了有关统计量的统计分布及效的估计。     

Global optimization using a synchronization of multiple search Points autonomously driven by a chaotic dynamic model

In the present paper, we propose a new multipoint type global optimization model using a chaotic dynamic model and a synchronization phenomenon in nonlinear dynamic systems for a continuously differentiable optimization problem. We first improve the Discrete Gradient Chaos Model (DGCM), which drives each search point’s autonomous movement, based on theoretical analysis. We then derive a new coupling structure called PD type coupling in order to obtain stable synchronization of all search points with the chaotic dynamic model in a discrete time system. Finally, we propose a new multipoint type global optimization model, in which each search point moves autonomously by improved DGCM and their trajectories are synchronized to elite search points by the PD type coupling model. The proposed model properly achieves diversification and intensification, which are reported to be important strategies for global optimization in the Meta-heuristics research field. Through application to proper benchmark problems [Liang et al. Novel composition test functions for numerical global optimization. In: Proceedings of Swarm Intelligence Symposium, 2005 (SIS 2005), pp. 68–75 (2005); Liang et al. Nat. Comput. 5(1), 83–96, 2006] (in which the drawbacks of typical benchmark problems are improved) with 100 or 1000 variables, we confirm that the proposed model is more effective than other gradient-based methods.     

Characterizing partition functions of the spin model by rank growth

We characterize which graph invariants are partition functions of a spin model over C$\mathbb{C}$, in terms of the rank growth of associated ‘connection matrices’.     

An elaborative unit cost structure-based fuzzy economic production quantity model

The purpose of this paper is to investigate and propose a fuzzy extended economic production quantity model based on an elaboratively modeled unit cost structure. This unit cost structure consists of the various lot-size correlative components such as on-line setups, off-line setups, initial production defectives, direct material, labor, and depreciation in addition to lot-size non-correlative items. Thus, the unit cost is correlatively modeled to the production quantity. Therefore, the modeling or the annual total cost function developed consists of not only annual inventory and setup costs but also production cost. Moreover, via the concept of fuzzy blurred optimal argument and the vertex method of the α-cut fuzzy arithmetic (or fuzzy interval analysis), two solution approaches are proposed: (1) a fuzzy EPQ and (2) a compromised crisp EPQ in the fuzzy sense. An optimization procedure, which can simultaneously determine the α-cut-vertex combination of fuzzy parameters and the optimizing decision variable value, is also proposed. The sensitivity model for the fuzzy total cost and thus EPQ to the various cost factors is provided. Finally, a numerical example with the original data collected from a firm demonstrates the usefulness of the new model.     

New approach to applying neural network in nonlinear dynamic model

In this work, radial basis function neural network (RBF-NN) is applied to emulate an extended Kalman filter (EKF) in a data assimilation scenario. The dynamical model studied here is based on the one-dimensional shallow water equation DYNAMO-1D. This code is simple when compared with an operational primitive equation models for numerical weather prediction. Although simple, the DYNAMO-1D is rich for representing some atmospheric motions, such as Rossby and gravity waves. It has been shown in the literature that the ability of the EKF to track nonlinear models depends on the frequency and accuracy of the observations and model errors. In some cases, just fourth-order moment EKF works well, but will be unwieldy when applied to high-dimensional state space. Artificial Neural Network (ANN) is an alternative solution for this computational complexity problem, once the ANN is trained offline with a high order Kalman filter, even though this Kalman filter has high computational cost (which is not a problem during ANN training phase). The results achieved in this work encourage us to apply this technique on operational model. However, it is not yet possible to assure convergence in high dimensional problems.