基于EMD-GA-BP与EMD-PSO-LSSVM的中国碳市场价格预测

由于碳交易市场价格的波动性大及相互影响关系的复杂性,本文试图构建碳价格长期和短期的最优预测模型。考虑到碳交易价格波动的趋势性和周期性特点,基于经验模态分解算法(EMD)、遗传算法(GA)—神经网络(BP)模型、粒子群算法(PSO)—最小二乘支持向量机(LSSVM)模型及由它们构建的组合预测模型,对中国碳市场交易价格进行短期预测和长期预测。实证分析中将影响碳交易价格的不同宏观经济因素和碳价格时间序列因素做为输入变量,分别代入组合模型进行预测。研究结果表明,在短期预测中,EMD-GA-BP模型预测效果优于GA-BP模型和PSO-LSSVM模型;而在长期预测中,组合模型EMD-PSO-LSSVM模型预测效果优于只考虑碳价格波动趋势性或周期性预测效果。     

考虑占位决策的围堵嫌犯模型

研究了2011年中国大学生数学建模竞赛B题的突发事件中交巡警对在逃嫌犯的围堵问题。不同于对该问题的以往的研究,本文考虑了交巡警在包围圈中可以占据某些路口,使得嫌犯不能通过这些被交巡警占据的路口,从而为形成包围圈的交巡警赢得更多时间。利用两篇相关文献的关于点截集判断的结论和考虑占位决策的建模方法,以不同的目标函数建立了考虑占位决策的围堵嫌犯问题的三个混合0-1非线性整数规划模型。通过选取部分线性约束和目标函数一起组合成混合0-1线性整数规划模型,设计了基于混合0-1线性整数规划方法的算法,并给出了算例。     

Using Neural Networks or Linear Models to Predict the Characteristics of Microcapsules Containing Phase Change Materials

Summary: Microcapsules with large amount of PRS® paraffin wax encapsulated and narrow size distribution were prepared by shirasu porous glass (SPG) emulsification technique and a subsequent suspension like polymerization process and then examined by DSC, laser diffraction and SEM analyses. An experimental design approach, based on a central composite design, was used to determine quantitatively the effect of PRS® paraffin wax/styrene mass ratio (PRS/St), percentage of polyvinylpyrrolidone/styrene mass ratio (%PVP/St) and water/styrene mass ratio (H₂O/St) on the microparticles properties. The results were fitted using two black-box models. The empirical equations allow the prediction of the amount of the paraffin wax encapsulated and the mean particle size in number as a function of aforementioned synthesis variables. It was observed that both models allowed to drawn the same conclusions. %PVP/St mass ratio was the most important parameter affecting the particle size distribution decreasing the average particle size with the increase of %PVP/St. On the other hand, PRS/St mass ratio has a direct influence on the latent heat of fusion.     

Improving weighted information criterion by using optimization

Although artificial neural networks (ANN) have been widely used in forecasting time series, the determination of the best model is still a problem that has been studied a lot. Various approaches available in the literature have been proposed in order to select the best model for forecasting in ANN in recent years. One of these approaches is to use a model selection strategy based on the weighted information criterion (WIC). WIC is calculated by summing weighted different selection criteria which measure the forecasting accuracy of an ANN model in different ways. In the calculation of WIC, the weights of different selection criteria are determined heuristically. In this study, these weights are calculated by using optimization in order to obtain a more consistent criterion. Four real time series are analyzed in order to show the efficiency of the improved WIC. When the weights are determined based on the optimization, it is obviously seen that the improved WIC produces better results.     

Stability in cellular neural networks with a piecewise constant argument

In this paper, by using the concept of differential equations with piecewise constant arguments of generalized type [1], [2], [3] and [4], a model of cellular neural networks (CNNs) [5] and [6] is developed. The Lyapunov-Razumikhin technique is applied to find sufficient conditions for the uniform asymptotic stability of equilibria. Global exponential stability is investigated by means of Lyapunov functions. An example with numerical simulations is worked out to illustrate the results.     

The design of robust value-creating supply chain networks: A critical review

This paper discusses Supply Chain Network (SCN) design problem under uncertainty, and presents a critical review of the optimization models proposed in the literature. Some drawbacks and missing aspects in the literature are pointed out, thus motivating the development of a comprehensive SCN design methodology. Through an analysis of supply chains uncertainty sources and risk exposures, the paper reviews key random environmental factors and discusses the nature of major disruptive events threatening SCN. It also discusses relevant strategic SCN design evaluation criteria, and it reviews their use in existing models. We argue for the assessment of SCN robustness as a necessary condition to ensure sustainable value creation. Several definitions of robustness, responsiveness and resilience are reviewed, and the importance of these concepts for SCN design is discussed. This paper contributes to framing the foundations for a robust SCN design methodology.     

一种快速且全局收敛的BP神经网络学习算法

目前误差反向传播(BP)算法在训练多层神经网络方面有很多成功的应用.然而,BP算法也有一些不足:收敛缓慢和易陷入局部极小点等.提出一种快速且全局收敛的BP神经网络学习算法,并且对该优化算法的全局收敛性进行分析和详细证明.实证结果表明提出的算法比标准的BP算法效率更高且更精确.     

用在线梯度法训练积单元神经网络的收敛性分析

<正>1引言仅由加和单元构成的传统前向神经网络已经广泛应用于模式识别及函数逼近等领域.但在处理比较复杂的问题时,这种网络往往需要补充大量的隐节点,这样就不可避免地增     

Star网络S_5的Hamilton圈分解

最近Star网络和Pancake网络作为超立方体(并行计算机中多处理机互连的一种著名拓扑结构)的替代品而被许多作者研究.这两种网络的一个好的特点是:与超立方体相比较,它们有较小的直径和顶点度.尤其Star网络,更是受到研究人员的极大关注.在本文中:(a)我们提出了一种在这两种网络中找Hamilton圈的新方法.(b)证明了关于Star网络S_n的一个猜想在n=5时是正确的,即给出了S_5的两个边不交的Hamilton圈,且S_5是这两个Hamilton圈的并.     

脉冲时滞细胞神经网络的周期解的存在性

利用重合度理论中的延拓定理和微分不等式技巧,给出了具有状态依赖时滞和分布时滞的脉冲细胞神经网络的周期解的存在性的充分条件.