初始条件优化的近似指数序列灰色建模方法

灰色GM（1,1）预测方法仅针对累加生成满足近似指数特点的原始序列建立预测模型。为了拓宽传统灰色预测模型的应用范围,设计了通过优化初始条件提高灰色 GM（1,1）预测精度的新方法——DGM（1,1,c,β）模型。对满足近似指数的原始序列建立DGM（1,1,c,β）模型,利用粒子群算法求解模型参数。最后,通过实例验证了所提出的DGM（1,1,c,β）预测模型的有效性和实用性。     

基于改进灰色模型预测的节流流量传感器测量校正方法

为了提高节流流量传感器的测量校正精度,提出一种基于改进灰色GM(1,1)模型的预测校正方法。该方法首先对灰色GM(1,1)模型的流程进行改进,并采用LM方法对GM(1,1)模型的初始值进行求解计算;然后利用基于Markov过程的残差修正模型进一步提高预测精度;最后通过实例分析验证了提出方法的可行性。实验结果表明,相比传统灰色GM(1,1)模型,提出改进模型的预测值与实际测量值的拟合度更好,有效降低了校正后的节流流量传感器的测量误差。     

基亏遗传算法的GM（1，1，λ）改进模型

研究表明,灰色GM（1,1）-N测模型中的背景值及建模序列光滑度对于模型的预测精度均有影响。针对这一问题,引入背景值构造参数入和三点平滑法,提出了基于三点平滑的GM（1,1,λ）改进模型,并利用遗传算法对引入的参数入进行了最优值搜索。最后通过实例和仿真证明改进模型优于现有的灰色模型,具有更高的模拟和预测精度。     

基于灰色神经网络的厦门市商品房需求量研究

介绍了灰色预测模型GM（1,1）的结构和模型检验,以及BP神经网络预测模型的原理,对灰色预测模型进行改良,将此改良模型与BP神经网络模型进行组合,建立了新的组合灰色神经网络模型。以厦门市商品房成交量为例,以MATLAB为工具,进行2012年的成交量对比以及2013年成交量的预测,结果证明组合灰色神经网络的预测精度较高,可以为房地产价格指数预测研究提供参考依据。     

基于蚁群优化的实时航迹灰色预测

对于空中目标实时航迹预测问题,在数据匮乏的情况下,灰色GM(1,1)预测模型是一种行之有效的方法,但实际工程应用中,灰色模型预测的精度时常达不到要求。通过对灰色模型内在原理逻辑的深入研究,指出了其初值和灰参数的选取并非最优。采用蚁群仿生算法对初值和灰参数进行优化,有效地提高了航迹预测的精度,通过实例计算验证了该方法的有效性和精确性。     

QPSO改进灰色融合模型的软件故障预测

为了提高小样本的预测精度,提出了一种基于量子行为粒子群优化与数据融合算法的灰色融合预测模型。首先从两个方面改进了GM(1,1)模型,对原始序列进行了幂函数变换,并采用量子行为粒子群优化算法实现了参数的优化。然后提出了多次建模的策略,利用原始序列的不同分量分别建立GM(1,1)改进模型进行预测,将各次预测值进行融合得到最终结果。最后用该模型进行软件故障预测,结果表明其相对误差在3%以内,适用于平滑性较差和高增长的序列预测。     

木材物流运输量GM(1,1)预测程序的设计

为了帮助基层林区政府更准确地预测木材运输量,研究了GM(1,1)模型的预测算法,发现其误差略大,离基准年份越远的预测值高于实际值的情况越严重。对其预测模型略做改进,将基准年份后移,降低了误差率。采用GM(1,1)和改进后GM(1,1)两种算法,分别对某县的木材物流量进行预测,和生产真实数据比较,改进后的GM(1,1)方法预测的最大相对误差为2.37%,精度有所提高。采用该模型计算量少,过程简单,精度高,可为县级基层林业局制作木材物流发展政策提供决策依据。     

基于遗传新陈代谢灰色模型的电子设备故障预测研究

经过研究分析,序列光滑度和背景值构造是常规灰色GM(1,1)模型误差的主要来源。针对这一问题,提出了一种改进的方法:首先对建模序列进行对数平滑处理,而后利用遗传算法对背景值进行寻优,并形成改进的新陈代谢GM(1,1,λ)模型。最后通过实例验证,表明改进的新陈代谢模型具有更高的预测精度,可用于电子设备的寿命预测。     

基于灰色GM（1,1）模型的城市人口数量预测

本文主要研究未来几年内人口数量组成变化的相关问题。对于该问题,我们在灰色GM（1,1）模型的基础上对部分算法进行了优化,考虑到常住人口和流动人口等类型,结合已有的连续5～10午的人口数量,最终给出了人口数量的预测结果。经检验证明,改进的灰色GM（1,1）模型能够有效解决人口数量预测问题。     

灰色数列模型在图像边缘趋势预测中的应用

为了达到跟踪预测刀具图像边缘变化趋势的目的,采用了灰色数列GM（1,1）模型预测拟合直线段斜率数列的方法。在分析灰色数列预测模型的基础上,通过建立了图像边缘点拟合直线斜率数列预测模型,并用建立的模型对一帧刀具图像进行边缘趋势预测,将得到的预测结果与实际值进行比较的试验。并以最大误差分析刀具后续边缘点分布趋势,为图像采集平台进给方向提供依据。根据实验结果,得出采用灰色数列模型预测刀具边缘轮廓方法完全满足平台跟踪刀具边缘轮廓要求的结论。     

基于灰色理论和神经网络建立预测模型的研究与应用

根据综合利用灰色理论与神经网络建立预测模型的思路,给出了利用神经网络对模型残差进行修正的灰色GM(1,1)模型综合预测方法。选用我国自1994年至2005年狂犬病发病率统计数据,用灰色GM(1,1)模型对历年的疾病发病人数进行建模,并利用LM算法优化的快速BP神经网络对其残差进行修正,使得建模和预测具有更高的准确性和适应性。实验结果及仿真验证表明,加入修正过程的综合模型,其预测效果远优于单一的灰色模型。     

应用GM(1,1)模型研究军用电子元器件的长期贮存寿命

现代武器装备“长期贮存,一次使用”的特性,要求装备的各个部件和元器件都具有良好的环境适应性与长期贮存寿命。根据灰色系统理论,以某电阻器为例,应用GM(1,1)模型及其改进模型预测了其长期贮存寿命,所建立的模型具有较好的精度,有一定的参考价值。实践证明：灰色预测方法简单、实用,具有一定的工程应用价值。     

一种新型电动车超级电容-蓄电池复合电源系统

针对目前电动车续驶里程短、电池易老化、爬坡能力差、能量回收效率低的缺陷,研究以比功率高、循环寿命长的超级电容作为辅助电源,实现了一种新型超级电容-蓄电池复合电源系统.从超级电容-蓄电池系统的结构出发,对电动车的行驶工况进行了具体分析,构建了超级电容充电模型和超级电容-蓄电池复合电源系统模型.仿真和实际试验结果表明,该复合电源系统能有效回收制动能量,从而使蓄电池的低能量密度和低功率密度等缺陷得到弥补,进而提高电动车动力性能和电能的利用率,使电动车的续驶里程增长,能有效地降低蓄电池电压和电流幅值波动,延长蓄电池的使用寿命.     

FPGA-based design of advanced BMS implementing SoC/SoH estimators

Energy storage system, usually a battery, become essential part for all electric drive vehicles such as hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV) and electric vehicle (EV) in the coming decades. These energy storage systems include Li-ion batteries, Ni-MH batteries, lead-acid batteries and ultra-capacitors. An accurate Battery Management System (BMS) is highly demanded integrated system in all electric derive vehicles to ensure the optimum use of an energy storage system. The battery's state monitoring & evaluation, charge control and cell balancing are the important features of any BMS. However, due to unavailability of inaccurate battery's state-of-charge (SoC)/state-of-health (SoH) estimators and uncertainty of battery's performance, new approaches of BMS design are under development to control batteries optimally and hence, the vehicle performance. In addition, most of the existing BMSs either do not provide SoH at all or provide it as a function of capacity degradation over the battery usage. This research paper presents the field-programmable gate array (FPGA) - based Advanced BMS design using MATLAB-to-FPGA design flow. The Advanced BMS design provides the combined estimation of both SoC and SoH of a rechargeable battery. This research paper also summarizes the Neuro-Fuzzy & statistical models implemented in Advanced BMS for accurate estimation of battery's SoC & SoH respectively. Further, this research paper presents the selection of suitable FPGA and its hardware realization implementing Advanced BMS. Finally, the experimental results are confirmed by simulation and synthesis of its register transfer level (RTL) design. FPGA-based Advanced BMS would provide the best chip solution for a generalized BMS with benefits of low Non-recurring engineering (NRE) cost, low power consumption, high speed of operation, large reconfigurable logic and large data storage capacity.     

Power-Electronics-Based Solutions for Plug-in Hybrid Electric Vehicle Energy Storage and Management Systems

Batteries, ultracapacitors (UCs), and fuel cells are widely being proposed for electric vehicles (EVs) and plug-in hybrid EVs (PHEVs) as an electric power source or an energy storage unit. In general, the design of an intelligent control strategy for coordinated power distribution is a critical issue for UC-supported PHEV power systems. Implementation of several control methods has been presented in the past, with the goal of improving battery life and overall vehicle efficiency. It is clear that the control objectives vary with respect to vehicle velocity, power demand, and state of charge of both the batteries and UCs. Hence, an optimal control strategy design is the most critical aspect of an all-electric/plug-in hybrid electric vehicle operational characteristic. Although much effort has been made to improve the life of PHEV energy storage systems (ESSs), including research on energy storage device chemistries, this paper, on the contrary, highlights the fact that the fundamental problem lies within the design of power-electronics-based energy-management converters and the development of smarter control algorithms. This paper initially discusses battery and UC characteristics and then goes on to provide a detailed comparison of various proposed control strategies and proposes the use of precise power electronic converter topologies. Finally, this paper summarizes the benefits of the various techniques and suggests the most viable solutions for on-board power management, more specific to PHEVs with multiple/hybrid ESSs.      

On Discount Weighting Grey Modeling of GM(1,1)

1introductionInsystemscience,scholarsoftenusedarkorlightcolortodescribetheself-containedextentofinformation.Ingeneral,"white"meansthattheinformationiscomplete,and"back"meansthattheinformationisunknownatallwhile"grey"meansthattheinformationisincompleteorunknownforsure.Soanumber,whoseinformationisincomplete,iscalledgreynumberAnelement,whoseintbrmationisincomplete,iscalledgreyelement;theincompletemutualrelationbetweensystemsorfactorsiscalledgreyrelation;and,iftheinformationisincompleteorunknownfo…     

电动车混合电源系统设计

设计了一种用于电动车的混合电源系统。该系统以蓄电池为主电源,采用具有双电层结构的超级电容器为辅助能源。利用单片机控制电动车在不同状态选择不同电源进行供电。以200 kg的人与车沿着仰角为30°,落差为6 m的坡度行驶进行实验,可增加130 m的行驶距离。这表明该混合电源有效地增加了电动车的行驶里程,同时可延长蓄电池的使用寿命,具有很大的现实推广意义。     

一种应用于电动汽车的双向DC／DC变换器研究

分析和比较了四种非隔离型双向DC／DC变换器的拓扑结构。针对目前广泛使用的混合动力电动汽车存在的环保问题和电池性能问题设计了一种双向DC／DC变换器一两相交错双向DC／DC变换器,论述了其工作原理,并通过一台3000W的样机验证了理论的正确性。     

基于多普勒激光雷达的风场预测

采用灰色理论、BP神经网络、布朗三次指数平滑算法来对雷达风场预测进行研究。利用香港国际机场激光雷达数据对风场PPI扫描风速进行预测,得到了预测时刻机场区域整个风场的概况,让飞行员能提前了解机场区域内未来一段时间的风场环境;使用下滑道扫描数据对飞机在下滑道附近的迎头风进行预测,从而更精确地预测飞机在起飞和降落过程中将会遇到的风场波动,使飞行员能够在风切变探测系统告警之前,增加飞行员进行反应和相关准备的时间。对实验结果的统计分析表明:布朗三次指数平滑预测在延长预测时间时,预测精度随时间的增加而下降的趋势较明显;灰色预测和BP网络预测在风场变化很大的情况和延长预测时间时,仍能在一定程度上保证预测精度;灰色预测较BP网络预测效果稍好。     

ADVISOR-based model of a battery and an ultra-capacitor energy source for hybrid electric vehicles

An energy source is the heart of a hybrid electric vehicle. If it is capable of supplying enough power at all times, then it is an adequate source. Major problems presently facing the industry include the size, cost, and efficiency of the energy source. The primary energy source presently used in automotive systems is a battery. In order to reduce the cost of the battery, the current needs to be decreased and stabilized so it is not very erratic. The purpose of this paper is to introduce and justify the use of a new model for an energy source: a battery in parallel with an ultra-capacitor. The ultra-capacitor can supply a large burst of current, but cannot store much energy. Conversely, the battery can store mass amounts of energy; however, without expensive and inefficient units, a battery cannot provide the current that the ultra-capacitor can. By combining the two energy sources in parallel, the storage and peak current characteristics desired can be achieved. The standards of the vehicle are not degraded, allowing this to be a promising technique to incorporate into hybrid electric vehicles to reduce their cost and increase the efficiency of their energy-source system.