对比发现，利用组合灰色神经网络模型预测的位移值较单独的灰色模型预测的位移值具有更高的精度。
It is more accurate of the forecasting results by the composite gray neural network model than that by the only gray models by comparison.
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。
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.
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。
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.
利用人工神经网络的方法实现系统云灰色模型的参数白化，提出了系统云灰色神经网络模型SCGNNM(1,1)，并给出了相应的学习算法。
By using neural networks as the approach for whitening system cloud gray model, the system cloud gray neural network models SCGNNM (1, 1), were proposed in this paper.
给出了灰色关联分析及BP神经网络进行判别分析的数学原理和模型。
The mathematical principles and models of gray relational analysis and BP neural network used to determine the source of water were introduced.
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。
Such as the return of specific prediction method, smoothing index, grey model prediction, BP neural network, RBF neural network .
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。
A new neural network model is established based on the concept of equal dimension and new information in grey theory.
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。
A synthetic condition prediction model is presented, using neural network and grey theory together make it possible to predict accurately.
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。
The establishment of the BP artificial neural network and grey system united model and grey data pretreatment depends on BP artificial neural network interpolate.
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.
文章根据组合预测的理论和BP神经网络对非线性数据良好的逼近特性，提出了基于BP神经网络的灰色预测、多项式回归模型的民用汽车运力组合预测模型。
Based upon the theory of combined forecasting, up-standing identity of BP neural network on approaching non-linear data, put forward a combined forecasting model for civil motors.
采用灰色关联分析法和BP神经网络法对样本进行建模，建立了适用于工区地层钻头优选的计算模型。
Gray relational analysis and BP neural network to model the samples was established in the work area for optimal formation bit computing model.
最后，对两种预测模型的结果进行了对比，验证并联型灰色神经网络预测模型的可行性。
Finally, the forecast results are compared between the two kinds of models. The comparison results indicate the feasibility of PGNN model.
对时间序列的一类预测模型进行了研究，把灰色模型与BP神经网络模型组合建模，通过实例分析取得好的效果。
In this paper, by using the gray system theory and the dynamic BP neural network, the combination forecasting model are discussed.
仿真结果表明，灰色新陈代谢BP神经网络的预测精度较BP神经网络和灰色新陈代谢模型明显提高；
Simulation results show that the grey metabolism BP prediction model improves the prediction accuracy significantly compared with BP and grey metabolism model;
仿真结果表明，灰色新陈代谢BP神经网络的预测精度较BP神经网络和灰色新陈代谢模型明显提高；
Simulation results show that the grey metabolism BP prediction model improves the prediction accuracy significantly compared with BP and grey metabolism model;
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