灰色系统理论以其方便有效的建模方法，在许多领域获得了广泛的有效的应用，但是在图像处理领域却很少见。
The grey system theory has found extensive application in a lot of fields with its convenient and effective modeling method, but rare in the field of image processing.
本文引入灰色系统理论，利用有限的时间序列，按照GM(1,1)建模方法，建立起黑龙江省污水总量长期预测模型。
This paper inserts grey system, makes use of finite time series, follows GM (1, 1) building method, builds the long term prediction model of total waste -water in Heilongjiang Province.
目前灰色系统理论在建立GM（1，1）模型时通常采用假定拟合曲线通过建模数据第一点来确定积分常数，从而得到预测公式的方法。
When grey systematic theory at present set up GM (1 , 1 ), it assumes that fit curve passes the first point of modeling data to confirm the integral constant, thus obtained to forecast formula.
结果表明，利用稳健估计理论来进行林业灰色建模，不但可以抗击粗差的影响，而且预测精度基本满足在80%以上。
The results showed that the application of Robust Estimation Theory in forestry gray modeling could not only avoid the effect of gross error, but also reach the predicted precision over 80%.
简述了灰色理论中灾变预测的特点及其建模过程，并将其应用于企业安全事故预测之中。
The set up process of gray theory is described, as well as the disaster forecast and its application in accident forecast.
本文利用邓聚龙提出的灰色系统理论的GM(1,1)模型对油田递减规律建模。
In this paper a model of production declining was established by the application of the GM (1, 1) model in the gray system theory proposed by Mr.
本文应用灰色系统理论阐述了滑坡剧滑时间预测模型的建模方法。
This thesis describes the method of installating models for forecasting landslide time by applying the "Grey System" theory.
应用灰色系统理论对试验结果进行灰色建模和预测，达到了指导试验和提高试验精度的目的。
The test results were modeled and forecasted by applying. the Grey System Theory for guiding the test process and improving test precision.
针对露天矿运输系统行车事故发生频次数据少的缺点，采用了灰色系统理论对其进行灰建模，灰预测。
According to the travel accident occurrence frequency the defect of open mining transportation system, the grey theory is used, the grey forecasting model is established and forecast.
基于灰色理论和自适应数据融合技术的研究，提出一种基于自适应数据融合的新型灰预测GM(1,1)模型，并对整个建模过程进行了理论推导。
A new modeling method of GM (1, 1) was proposed based on the research of grey theory and adaptive data fusion. The whole modeling procedure of this method was established.
在用遗传方法对灰色理论建模数据进行全局优化的基础上，建立了预测冻结法施工中外层井壁承受冻结压力发展趋势的智能灰色理论模型。
On the basis of the global optimization of data which is used to build up mold, the artificial intelligent grey theory mold was established to forecast the freeze pressure development trend.
灰色预测控制理论中灰色建模和“超前控制”的思想较好地弥补了线性最优控制理论中精确线性化和“事后控制”的不足。
The grey modeling and the idea of pre-control in grey prediction control theory can well remedy the defects of exact linearization and after-control in linear optimal control theory.
简要介绍了灰色理论的基本原理及灰关联分析、灰色聚类、灰色预测、灰色建模四种基本方法。
This paper discusses mainly the principal of Grey Theory and the method of relational grade analyses, Grey clustering, Grey prediction and Grey modeling.
本文基于灰色系统理论，提出一种残商辨识修正的灰色建模方法，这种方法精度高、算法简单。
Based on the gray system theory, the paper suggests a kind of gray modeling method with incomplete-quotient distinguished correction, which is characterized by high accuracy and simple algorithm.
用MATLAB语言将灰色系统理论中的GM（1，1）模型，从建模到模型的检验过程编制成一个普适程序。
MATLAB program, which can be used in the whole procedure of modeling and testing a gray model GM(1,1), was designed in this paper.
研究了灰色系统理论与神经网络组合的灰色神经网络GNNM（1，1）模型的建模思想、网络结构及其优化GNNM（1，1）模型的方法和学习算法；
Study modelling thought, network configuration, majorize GNNM(1,1) mode method and learning algorithm of GNNM(1,1) mode combined grey system theory and neural network.
利用灰色理论的建模预测方法对随机性较大的数据预测精度不高；
The prediction accuracy of grey theory is limited by the dam's randomness.
本文采用系统分析、灰色系统理论和人工智能理论方法对我国能源需求进行了建模与分析。
This paper adopts the methods of systematic analysis, grey system theory and artificial intelligence theory to construct China's energy demand model.
灰生成是使灰过程变白的数据变换方法，能为灰预测建模提供中间信息，并弱化原始数据的随机性，在灰色系统建模理论中具有显赫的地位。
Grey generating is a method of transfering the grey process into white process. It can provide middle information for grey forecasting modeling and weaken the randomness of the raw data.
灰生成是使灰过程变白的数据变换方法，能为灰预测建模提供中间信息，并弱化原始数据的随机性，在灰色系统建模理论中具有显赫的地位。
Grey generating is a method of transfering the grey process into white process. It can provide middle information for grey forecasting modeling and weaken the randomness of the raw data.
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