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.
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.
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.
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.
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 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.
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.
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.
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