关于灰色系统建模公式的改进

本文从理论角度对灰色系统建模公式进行了讨论,证明灰色系统建模公式仅仅是一个近似运算公式,并在此基础上,推导出灰色系统GM(1,1)模型的改进模型GM’(1,1).通过运算结果比较,证明改进模型比原系统模型具有更高的预测精度.     

应用灰色模型预测加工误差的研究

基于机械加工误差的特点 ,利用灰色系统理论建立轴承磨削加工过程尺寸误差动态分析的数学模型。根据该模型分析 ,预测了当前和未来的加工尺寸的动态分布。结果表明 ,模型精度高 ,计算值与实测值能够较好地吻合     

基于灰色理论的电厂设备状态趋势预测研究

本文提出了基于灰色预测理论的机械设备状态监测方法,重点介绍了灰色预测理论的内容及其应用;其根据设备自身历史数据建立动态微分方程,并预测自身的发展,具有数据量小、计算简单、预测准确的特点.通过实例证明,该方法是有效的.     

灰色模型在疲劳寿命预测中的应用

疲劳寿命预测的问题是疲劳研究中的重要课题。影响疲劳寿命的因素多且复杂,利用灰色理论方法进行疲劳寿命预测,提出了非等间距GM(1,1)模型和中心逼近式GM(1,1)模型两种预测疲劳寿命的方法。通过对实验数据进行分析和整理,然后建立微分方程,利用MATLAB软件计算得到灰色预测值。与实验数据值进行比较,得出结果表明灰色模型方法具有很高的预测精度,证明灰色理论是一种简单可行的、可靠的分析方法。     

灰色系统理论在油管腐蚀寿命预测中的应用

利用灰色系统理论,对N80钢氮化在郝现联合站介质中的腐蚀数据进行了灰色分析,建立了对上述数据的GM(1,1)模型。经检验,此模型预测精度较高,对实际工程油管腐蚀寿命的研究起到指导作用。     

基于灰色预测的无模型控制在球磨机负荷控制中的仿真研究

火电厂钢球磨煤机的负荷对象具有大滞后、慢时变、强非线性等复杂特性,采用常规控制方法难以获得满意的控制效果,本文提出了基于灰色预测的无模型自适应负荷控制方法.该方法融合了无模型控制的自适应、抗干扰特性与灰色预测模型的预测时延、抑制超调和快速稳定特性,它将灰色模型的预测结果代替负荷对象输出测量值,再进行无模型自适应闭环控制.仿真结果表明这种控制方法系统响应快、超调小、鲁棒性好、抗干扰能力强,可以有效解决大滞后、非线性及适应性等问题.     

基于灰色理论的汽车大修期的预测

采用灰色系统理论中的GM(1，1)模型对汽车大修期里程表读数进行数值拟合和预测，结果显示，灰色系统理论与方法应用于汽车大修期里程表读数的处理是可行的，且预测精度较高。     

基于灰色理论的导弹贮存可靠性预测方法研究

介绍了导弹贮存可靠性的概念,针对导弹样本少、试验数据贫乏的特点。对于导弹贮存可靠性的预测问题。提出了具有“少数据建模”特性的灰色GM（1,1）预测方法。最后通过实例给出预测过程。     

基于灰色线性回归组合模型的故障率预测

针对武器装备在使用过程中故障率变化的复杂性,提出了灰色线性回归组合模型的故障率预测方法。该模型用线性回归方程和指数方程的和来拟合故障率曲线,它可以改善线性回归模型中没有指数增长趋势和GM（1,1）模型中没有线性因素的不足。通过对装备备件故障率的预测分析表明,灰色线性回归组合模型在故障率预测精度上优于单一的灰色模型和线性回归模型,且不要求对所提供的历史数据具有典型的分布规律。该模型的预测结果可以为装备的维修工作提供决策依据。     

试验数据处理的多因素灰色模型GM(1,N)及其应用

在分析试验数据处理的现状后，运用灰色系统理论，通过优化模型系数与背景取值，建立了多因素灰色GM（1，N）模型。该模型克服了现有GM（1，N）模型的不足，扩宽了GM（1，N）模型的应用范围，具有精度高、使用简便等特点，计算实例表明，该模型简单实用，值得在试验数据处理中推广应用。     

MGM(1,N)模型在结构载荷-应变关系中的应用

由于在役结构受到外部各种因素的影响,使其结构参数及材料性能都发生了变化,而这种变化对结构的力学性能的影响又是不确定的,因此结构在实际工作状态下的强度难于用一个确定的数学公式表达清楚。本文运用灰色系统理论的多变量动态灰色建模方法建立了在役结构在实际工作状态下的载荷-应变关系的预测模型,并对失效荷载进行了预测,计算得到了结构的失效载荷及在该载荷作用下的应变,与实测结果十分吻合。因此运用多因素灰色系统理论建立的预测模型MGM(1,N)具有较高的拟合精度和预测精度,在其它工程领域的数据处理中具有广阔的应用前景。     

线图数据处理的灰色模型与方法

在分析计算机辅助设计产数据处理的现状后，提出了其线图数据处理的GM（1，1）模型参数估计的函数变换法。该模型不仅适用于等间距也适应用于非等间距的线图。实例表明，该方法简单实用，值得在计算机辅助设计中推广使用。     

Identification of uncertain nonlinear systems for robust fuzzy control

In this paper, we consider fuzzy identification of uncertain nonlinear systems in Takagi-Sugeno (T-S) form for the purpose of robust fuzzy control design. The uncertain nonlinear system is represented using a fuzzy function having constant matrices and time varying uncertain matrices that describe the nominal model and the uncertainty in the nonlinear system respectively. The suggested method is based on linear programming approach and it comprises the identification of the nominal model and the bounds of the uncertain matrices and then expressing the uncertain matrices into uncertain norm bounded matrices accompanied by constant matrices. It has been observed that our method yields less conservative results than the other existing method proposed by S?krjanc et al. (2005) [11] and [12]. With the obtained fuzzy model, we showed the robust stability condition which provides a basis for different robust fuzzy control design. Finally, different simulation examples are presented for identification and control of uncertain nonlinear systems to illustrate the utility of our proposed identification method for robust fuzzy control.     

Adaptive control of the WEDM process using a self-tuning fuzzy logic algorithm with grey prediction

A control system to improve the efficiency of machining a workpiece with varying thickness in the wire electrical discharge machining (WEDM) process is proposed. The abnormal ratio R _ab defined by the proportion of abnormal sparks in a sampling period is taken as the controlled variable. It is allowed to reduce temporarily as the cutting thickness is changing. A gain self-tuning fuzzy control algorithm is used so that the transient situation as the cutting thickness is suddenly increasing can be suppressed immediately, and a stable performance can be achieved. In addition, the grey predictor is adopted to compensate the time-delayed R _ab caused by the low-pass filter data processing. Experiments reveal that there is a slight variance in the optimal reference of R _ab when the cutting thickness is larger than 20 mm, and its value is set to 55% in these cases. Three cases were tested: the constant machining parameters, the constant R _ab and the proposed adaptive R _ab . The results show that the cutting speed can be obviously improved by the proposed control strategy.     

基于改进的灰色理论在中长期电力负荷预测中的应用

面对中长期电力负荷预测“小样本”、“少信息”、“非线性”等特点[1],灰色预测模型在电力负荷预测中起决定性的作用。该文论述了基本GM（1,1）模型、一次平滑法的GM（1,1）模型。针对以上两种模型的缺点和不足,通过对初始数据的二次平滑处理,又提出了改进的灰色预测模型---二次平滑法的GM（1,1）模型。通过算例检验与典型的实例研究上述3种灰色模型在中长期负荷预测中的应用。结果表明,和前面两种预测模型相比较,二次平滑法的GM（1,1）模型在电力系统电量的实际预测中更精确,误差更小。     

Self-tuning fuzzy control with a grey prediction for wire rupture prevention in WEDM

Wire rupture in the wire electrical discharge machining (WEDM) process is one of the most troublesome problems in practical applications. In this paper, the abnormal ratio R_ab, defined as the proportion of abnormal sparks in a sampling period, is taken to represent the gap state in machining. The grey predictor is adopted to compensate the time-delayed R_ab caused by the low pass filter data processing. A gain self-tuning fuzzy control system has been developed to cope with the conditions that often occur with wire rupture in the WEDM process, such as an improper setting of machining parameters, machining the workpiece with varying thickness, etc. Experimental results of several cases show that the proposed controller results in a satisfactory performance. Not only can it immediately suppress transient situation once there is a sudden change of workpiece thickness, but a stable performance can also be achieved during machining a workpiece of constant thickness. As a result, wire rupture problems in most WEDM processes can be successively solved by the proposed control strategy.     

Self-organizing fuzzy control of constant cutting force in turning

Constant force control is gradually becoming an important technique in the modern manufacturing process. Especially, constant cutting force control is a useful approach in increasing the metal removal rate and the tool life for turning systems. However, turning systems generally have nonlinear with uncertainty dynamic characteristics. Designing a model-based controller for constant cutting force control is difficult because an accurate mathematical model in the turning system is hard to establish. Hence, this study employed a model-free fuzzy controller to control the turning system in order to achieve constant cutting force control. Nevertheless, the design of the traditional fuzzy controller (TFC) presents difficulties in finding control rules and selecting an appropriate membership function. Moreover, the database and fuzzy rules of a TFC are fixed after the design step and then cannot appropriately regulate ones real time according to the system output response and the desired control performance. To solve the above problem, this work develops a self-organizing fuzzy controller (SOFC) for constant cutting force control to evaluate control performance of the turning system. The SOFC continually updates the learning strategy in the form of fuzzy rules, during the turning process. The fuzzy rule table of this SOFC can be begun with zero initial fuzzy rules which not only overcome the difficulty in the TFC design, but also establish a suitable fuzzy rules table, and support practically convenient fuzzy controller applications in turning systems control. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old lathe for a turning system to evaluate the feasibility of constant cutting force control. The SOFC has a better control performance in constant cutting force control than does the TFC, as verified in experimental results.     

Self-organizing fuzzy control of constant cutting force in turning

Constant force control is gradually becoming an important technique in the modern manufacturing process. Especially, constant cutting force control is a useful approach in increasing the metal removal rate and the tool life for turning systems. However, turning systems generally have nonlinear with uncertainty dynamic characteristics. Designing a model-based controller for constant cutting force control is difficult because an accurate mathematical model in the turning system is hard to establish. Hence, this study employed a model-free fuzzy controller to control the turning system in order to achieve constant cutting force control. Nevertheless, the design of the traditional fuzzy controller (TFC) presents difficulties in finding control rules and selecting an appropriate membership function. Moreover, the database and fuzzy rules of a TFC are fixed after the design step and then cannot appropriately regulate ones real time according to the system output response and the desired control performance. To solve the above problem, this work develops a self-organizing fuzzy controller (SOFC) for constant cutting force control to evaluate control performance of the turning system. The SOFC continually updates the learning strategy in the form of fuzzy rules, during the turning process. The fuzzy rule table of this SOFC can be begun with zero initial fuzzy rules which not only overcome the difficulty in the TFC design, but also establish a suitable fuzzy rules table, and support practically convenient fuzzy controller applications in turning systems control. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old lathe for a turning system to evaluate the feasibility of constant cutting force control. The SOFC has a better control performance in constant cutting force control than does the TFC, as verified in experimental results.