一种基于指数平滑的天然气产量灰色预测模型(英文)

常规灰色预测模型所需样本数据量较少,容易对预测有用的信息量隐含丢失,在天然气产量预测应用中存在着预测精度不高的问题。应用指数平滑法对天然气产量样本数据进行处理,既充分利用样本中的有用信息,又减少其随机性;然后再对灰色模型的背景值计算方法进行了改进,将样本数据变换成规律性强的呈指数变化的序列,从而提出了一种基于指数平滑改进的天然气产量灰色预测模型。实例分析表明,该模型比常规灰色预测模型有更高的预测精度。     

基于混合伽玛分布的Bayes可靠性评估方法

研究指数寿命产品验前信息的融合问题。为了合理地利用验前信息,同时兼顾产品可靠性试验的不确定性,提出运用混合伽玛分布作为验前分布的贝叶斯方法,并将之推广到多源验前信息和变总体的情况。重点解决多源验前信息融合权重的确定问题。通过实例,分析比较这一方法与经典方法和单纯使用伽玛分布作为验前分布的贝叶斯方法的差异,证明该方法的合理性和可行性。     

三个电耦合神经元系统同步分析和模拟

研究了三个线形和环形耦合HR神经元网络模型的同步行为.通过构造合适的Lyapunov函数和LMI方法,获得线性化偏差系统零解的全局渐近稳定性和全局指数稳定性,给出实现同步的相关判据.进一步地,环形耦合网络比线形耦合网络更容易同步.数值结果显示了我们结果的有效性.     

切换网络下时变线性多智能体系统的指数同步

研究切换网络下时变连续线性多智能体系统的同步问题. 在智能体动态满足一致完全可控性条件下, 设计出同步协议. 通过将多智能体系统同步问题表示为矩阵无穷乘积形式并论证矩阵无穷乘积指数收敛到0, 给出了多智能体系统指数同步结论. 最后以仿真实例验证本文结论.     

具有边界切换反馈控制的波方程网络及其稳定性分析

本文主要就具有两种不同切换反馈控制的网络系统进行探讨.该系统是由波方程所描述的二叉树结构,其中,系统的一端固定,另外两端分别施加弹性反馈控制α(t)υ_(2t)(x,t),β(t)υ_(3t)(x,t),并且α(t),β(t)都是以2T为周期的Heaviside型函数.考虑两种情况下闭环系统的稳定性:一种是两个切换控制同时开同时关的情况,一种是两个控制器不同时开关的情况,即在任意的时间段只有一个控制器在工作.本文利用达朗贝尔方法给出闭环系统的具体的解的表达式,然后通过迭代方法对产生的迭代矩阵进行谱分析,由此得到系统的解的性质,从而得到了系统的指数稳定性和衰减速率.     

广义的时滞细胞神经网络的动态分析

本文研究了广义细胞神经网络的动态行为,首先利用度理论建立了客中广义时滞细胞神经网络的平衡点存在唯一物充要条件,其次给出这种广义时滞神经网络全局指数稳定的充要条件。     

Exponential stability analysis of travelling waves solutions for nonlinear delayed cellular neural networks

In this paper, the exponential stability of travelling waves solutions for nonlinear cellular neural networks with distribute delays in the lattice is studied. The weighted energy method and comparison principle are employed to derive the sufficient conditions under which the networks proposed are exponentially stable. Following the study [13 X. Liu, P. Weng, and Z. Xu, Existence of traveling wave solutions in nonlinear delayed cellular neural networks, Nonlinear Anal. Real World Appl. 10(1) (2009), pp. 277–286.[Crossref], [Web of Science ®] , [Google Scholar]] on the existence of the travelling wave solutions in nonlinear delayed cellular neural networks, this paper is focused on the exponential stability of these travelling wave solutions.     

Determining the initial distribution in space-fractional diffusion by a negative exponential regularization method

In this paper, we consider the backward problem for diffusion equation with space fractional Laplacian, i.e. determining the initial distribution from the final value measurement data. In order to overcome the ill-posedness of the backward problem, we present a so-called negative exponential regularization method to deal with it. Based on the conditional stability estimate and an a posteriori regularization parameter choice rule, the convergence rate estimate are established under a-priori bound assumption for the exact solution. Finally, several numerical examples are proposed to show that the numerical methods are effective.     

Combination of forecasts for the price of crude oil on the spot market

In this paper, we present a combination of three forecast models, ARIMA, exponential smoothing and dynamic regression, in order to predict the West Texas Intermediate (WTI) crude oil spot price and the Brent North Sea (Brent) crude oil spot price. Using samples from the period between January 1994 and June 2012 (in-sample), we identify the parameters and estimate the models. The validated models are combined to perform a forecast out-of-sample between July 2012 and June 2013. The results demonstrate that among the three models tested in-sample with Brent Prices, based on the MAPE measurement error, the ARIMA (2,1,8) model produced the best result, and the dynamic regression model was the best in-sample model for the WTI price. In the validation phase, the dynamic regression models did not prove to be valid, and therefore the combinations are performed only with the ARIMA and exponential smoothing models. For both proxies of oil prices, the combination of forecasts using ARIMA and exponential smoothing (out-of-sample) performed better than individual ARIMA and exponential models and also better than our benchmark models (naive forecast and Neural Network model). Based on the results, it can be inferred that using to a combination of forecasts to predict WTI and Brent spot prices is promising. We also point out that the selected model is easily replicable in spreadsheets and forecasting software and is based only on the past or lagged WTI or Brent values for future predictions.     

高速公路裂缝边缘检测算法的研究

本文提出了一种有效的高速公路裂缝边缘快速检测算法。通过将对称指数滤波器（ISEF）算法与边过程算法有机结合的方法,对高速公路裂缝图像进行抑制强烈噪声和微分二值化运算,并利用追踪围线提取得到裂缝边缘。实验结果证明,该方法能稳定有效地提取图象中的连续裂缝边缘,并且运算复杂度是线性的,有利于高速实时的处理。