Therefore, combining grey system with neural network, the grey neural network can make up the shortage of using single model to achieving excellent data processing and predictive validity.
故将灰色系统和神经网络有机融合，形成灰色神经网络模型，能弥补单一使用这两种模型时的不足，达到优良的数据处理和预测效果。
According to the speciality of electricity demand development in a city, the grey neural network model GNNM (1, 1) was introduced into the field of city electricity demand forecasting in this paper.
针对城市电力系统年用电量增长的特点，将灰色神经网络模型GNNM(1,1)引入城市年用电量预测。
This article introduces the grey forecast technology and the artificial neural network forecast technology in detail.
本文较为详细的介绍了灰色预测技术和人工神经网络预测技术。
Such as the return of specific prediction method, smoothing index, grey model prediction, BP neural network, RBF neural network .
具体如回归预测法、指数平滑法、灰色模型预测法、BP神经网络法、RBF神经网络法。
The article analyses original methods of DGA fault diagnose, include three-ratio method, artificial neural network method, Fuzzy Theory, diagnosing expert system, grey relational algorithm, et.
本文分析了原有的DGA故障诊断方法，包括三比值法、人工神经网络法、模糊理论、变压器诊断专家系统、灰色关联算法等。
It is found that it is better to predict the return rate with general regression neural network than with grey prediction and multiple regression model.
再以广义回归神经网络建立预测模型，与灰预测模型、多元回归模型进行预测能力及报酬率的比较分析。
The establishment of the BP artificial neural network and grey system united model and grey data pretreatment depends on BP artificial neural network interpolate.
使用BP神经网络插值方法对灰色数据进行了预处理，进而建立了预测软基沉降量的BP神经网络和灰色系统联合模型。
Then, the grey prediction methods and neural network prediction methods are researched, and these methods are used to conduct comparison of prediction about existing data sets in the paper.
然后，深入的研究了灰色理论预测方法和神经网络预测方法，并使用这些方法对现有数据集进行对比预测。
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.
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；
A new neural network model is established based on the concept of equal dimension and new information in grey theory.
采用灰色理论中的等维新息思想构建训练样本，建立了等维新息神经网络预测模型。
Case study shows that this method is more accurate and faster than single grey prediction and single neural network method. It is a useful method for long term load forecasting.
最后采用我国某省年用电量的预测的算例表明该方法的预测精度优于单一的灰色预测和单一的神经网络预测方法，为电力系统长期负荷预测提供了一种有用的方法。
Based on the review of traditional prediction methods, the paper forward a system composed of grey theory and neural network theory, and an ameliorative method on its function is studied.
在分析考察传统预测分析方法的基础上，本文提出了一个由灰色理论和神经网络理论组合的预测系统，并针对系统性能的改善和提高进行了深入的研究。
Based on ideals of improving on forecasting precision of the combination grey theory and neural network, the technology of the combination was given.
基于灰色理论与神经网络相结合能改进预测精度的思想，本文阐述了灰色理论和神经网络相结合的技术。
A synthetic condition prediction model is presented, using neural network and grey theory together make it possible to predict accurately.
提出了设备运行状态综合预测模型，神经网络和灰色理论的组合应用，提高了状态预测的准确性。
Then the author analyzes Grey System Theory, relative analysis method and the development of Artificial Neural Network.
然后系统地分析了灰色系统理论、关联分析方法以及神经网络理论的原理、功能。
Recently grey prediction method and neural network, which have still great research potential, are the hot topic in the field of FVF research.
灰色预测法和神经网络法是当前预测研究领域中的热点，有很大的研究空间。
Recently grey prediction method and neural network, which have still great research potential, are the hot topic in the field of FVF research.
灰色预测法和神经网络法是当前预测研究领域中的热点，有很大的研究空间。
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