中文摘要：

针对动态测量系统的时变性和不确定性,将动态测量序列作为灰色过程处理,采用参数随时间变化的灰色新陈代谢模型序列代替传统的单一模型,建立动态测量系统的非统计数学模型。通过算例和仿真分析充分验证了灰色新陈代谢模型序列较传统的统计模型和模糊、神经网络等非统计模型能更快速、平稳、准确地描述动态测量系统的特性,并且有较好的适应性,其建模方法同样适用于静态测量系统。灰色非统计少数据、生成序列建模,消除了对大样本量和典型概率分布的依赖性,以模型的动态性表征系统动态性,其建模过程简单、计算量小、实时性强。

英文摘要：

Aiming at the uncertainty and time-variation of dynamic measurement system, a new non-statistical modeling method is presented. The dynamic measurement sequence is regarded as a grey process ; and a series of metabolic grey models, whose parameters change with time, are used to describe the dynamic characteristics of the system, instead of using traditional single fixed model. Calculation example and simulation analysis prove that the metabolic grey model sequence can describe the features of the dynamic measurement system faster, more steadily and more accurately than traditional statistical model and non-statistical models such as fuzzy or neural network models. The grey non-statistical modeling of dynamic measurement system is a data generation modeling method with less data. Therefore, it eliminates the needs for large samples and canonical distribution in modeling. The method uses dynamic model to describe the system dynamic characteristics, which is simple to process and features less calculation and strong real time characteristics.