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.
本文从偿付能力监管的角度，以中国人寿为例，运用灰色神经网络理论，建立了偿付能力风险预警系统。
In this paper, from solvency supervise Angle, take China Life Insurance Company for example, applying gray neural network theory, establish an solvency risk early warning system.
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.
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.
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。
This article introduces the grey forecast technology and the artificial neural network forecast technology in detail.
将模糊数学、神经网络方法及灰色系统理论有机地结合起来，应用于金山金矿采矿方法选择中是一次新的尝试。
The combined use of fuzzy mathematics, neural net method and gray system theory in the selection of mining method for Jinshan gold mine was a new trial.
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 .
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。
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.
评述了灰色预测方法，概率统计方法，人工神经网络方法和可靠度函数分析四种国内外正在研究和使用的方法。
A review is made of the four methods being studied and used in China and other countries: gray prediction, probabilistic statistics, artificial nerve network, and functional analysis of reliability.
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.
本文就灰色系统理论与人工神经网络方法在预测中存在的问题进行了讨论。
This article the question which existed in the forecast has carried on the discussion on the grey systems theory and the artificial neural networks method.
此方法是用人工神经网络去把握灰色GM(1,1)所得到的预测值和实测值之间的未知关系，再进行新的预测。
It is designed to attach importance to the unknown connection between forecasting value and real value which is obtained by GM (1, 1) model with artificial neural networks and to forecast again.
利用灰色理论本身的特征对经济参数进行预测，并运用自适应模糊神经网络对其拟合误差进行预测，从而达到较好的预测效果。
This article utilizes the character of Gray Theory to predict the economy parameter, and then in order to obtain a better predicting effect, the ANFIS is employed to predict its simulating error.
并且分析了现有的一些石油需求预测方法，这些方法包括：时间序列法、人工神经网络方法、灰色系统法、弹性系数法等。
And some existing methods of oil demand forecast are analyzed, such as Time Series Analysis, Artificial Neural Network method, Gray System method and Consumption elasticity coefficient method.
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。
Based on ideals of improving on forecasting precision of the combination grey theory and neural network, the technology of the combination was given.
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.
结果表明，灰色理论能成功地对结构损伤进行预测，神经网络适用于此类损伤无规律对象问题的诊断。
The results show that while the gray system can be very successful in structural damage prediction, neural network technique is applicable to irregular structural damage detection.
神经网络方法、灰色系统理论、优化技术、现代控制论理论及动态odbc技术的应用是该系统设计的最大特点。
The system design is mostly characterized by using neural networks, gray system theory, optimization technology, modern control theory and dynamic ODBC technology.
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.
利用改进的BP神经网络对中国2000年、2005年、2010年的汽车保有量进行预测，并与灰色预测的结果进行对比。
The number of automobiles owned by China in 2000, 2005 and 2010 was forecasted based on BP neural network. The results by this method were compared with those of gray forecasting.
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.
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。
Then the author analyzes Grey System Theory, relative analysis method and the development of Artificial Neural Network.
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.
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。
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。
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.
应用推荐