A new concept of generalized fuzzy inference and the generalized fuzzy RBF network model are presented. The generalized Lear ni ng algorithm of this network model is derived.
在提出广义模糊推理概念的基础上，提出并分析了广义模糊径向基(rbf)神经网络模型，给出了该网络的广义学习算法。
Surface reconstruction for complex micro-landform is proposed using RBF network model in this paper and RBF network fit for curved surface reconstruction is accordingly established.
采用RBF网络模型进行复杂微地形曲面重构，建立了适应于曲面重构的RBF网络模型。
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。
In this paper, the model structure and the application of Radial Basis Function Neural Network (RBF NN) to fault diagnosis of power transformer is presented.
研究了径向基函数(RBF)神经网络的模型结构及其在电力变压器故障诊断中的实现方法。
Aimed at the problems of document automatic classification, a classification method is proposed based on fuzzy vector space model and RBF network.
针对文本自动分类问题，提出了一种基于模糊向量空间模型和径向基函数网络的分类方法。
One maneuvering target tracking method in which RBF neural network is applied to correct Kalman filter result in standard Interacting Multi Model (IMM) is proposed in this paper.
文中提出了一种应用rbf神经网络对标准IMM算法中的卡尔曼滤波结果进行校正的方法。
A model of rough radial basis function (RBF) neural network with attribute significance is presented.
提出一种基于属性重要性的粗糙rbf神经网络模型。
Based on the idea of data parallelism, a parallel training model for RBF (radial basis function) neural network in time-series prediction to improve the training speed is proposed.
根据数据并行的思想，提出了在时序预测中并行训练神经网络的模型，以提高训练速度。
The paper presents an immune clustering RBF neural network (ICRBFNN) model for short-term load forecasting.
提出了一种免疫聚类径向基函数神经网络(ICRBFNN)模型来预测电力系统短期负荷。
Energy consumption prediction is made by the use of radial basis function (RBF) neural network method, and energy consumption prediction model based on RBF neural network is established.
采用径向基函数(RBF)神经网络方法进行能源消费量预测，建立了基于RBF神经网络的能源消费量预测模型。
On the basis of macroscopic dynamic traffic flow model which is frequently used in traffic control, Radial basis Function (RBF) neural network is designed.
根据常用的高速公路交通流宏观动态模型，建立了高速公路交通流的RBF神经网络模型。
Then an end-point forecast model was built based on the RBF neural network by analyzing the melting technology and the RBF training algorithm.
然后通过对真空感应冶炼工艺机理的深入分析以及对神经网络算法的研究，建立了基于RBF神经网络的终点预报模型。
Using RBF (Radial Basis Function) network as the soft-sensing model, its natural to introduce regularization learning algorithm.
以径向基函数神经网络作为软测量模型，在软测量建模中引入正则化学习算法。
A prediction model of molten steel temperature based on RBF neural network was developed to reduce cost and improve temperature control accuracy for vacuum induction melting.
针对真空感应炉生产过程中温度测量成本较高及精度较差等不足，建立了基于RBF神经网络的真空感应炉终点钢水温度预报模型。
At last, this paper USES the RBF neural network that was optimized by the mentioned parallel model to predict the value of some nonlinear functions and the close of several stocks.
最后，本文应用上述并行模型优化的RBF神经网络对非线性函数值以及证券个股收盘价进行预测。
A new model of lake and reservoir eutrophication assessment can be established with RBF network of artificial neural network.
运用人工神经网络中的RBF网络算法，建立一种新的湖库水质富营养化程度评价模型。
The result also shows that RBF neural network laser fluorescence spectra data analysis identification model get the best perform in velocity, the average training time is 5.547 seconds.
神经网络方面，RBF神经网络激光荧光光谱分析识别模型的速度最快，平均训练时间为5.547秒。
The decay trend prediction model of mechanical equipment capability adopted single invisible layer feed forward network and chose the expansion speed of RBF, the network was established.
机械装备性能衰退趋势预测模型，采用单隐层前馈网络，通过径向基函数的扩展速度的选择创建网络。
RBF neural network is a three-layer feedforward network and can be used to identify nonlinear model effectively.
RBF神经网络是一种三层前向网络，可有效用来进行非线性模型的辨识。
Simulation results indicate that the modeling method by using the RBF neural network identification technique is effective with the established model featuring a relative high precision.
仿真结果表明采用RBF神经网络辨识建模的方法是有效的，建立的模型精度较高。
For RBF neural network model, which is more effective to monitoring weld quality than the others, the average error validated is 2.28% and the maximal error validated is under 10%.
径向基函数神经网络模型的监测准确率高于其他两种模型，其平均验证误差为2.28%，最大验证误差低于10%。
A new method was represented to model dynamic linear regression system driven by data, in which a bayesian network was combined with the RBF neural network.
结合贝叶斯网络和神经网络，提出了一种建立数据驱动型的动态线性回归系统模型的方法。
By analyzing the causes and contributing influences of sands liquefaction potential, the RBF neural network model for prediction is set up and compared with the BP network.
通过分析砂土液化成因及其影响因素，建立了砂土液化预测r BF网络模型，并与BP网络预测模型进行比较。
And damage situation of cable-stayed cable is simulated by using RBF network based on finite element model in ANSYS.
基于ANSYS有限元模型，采用RBF网络，模拟了斜拉索的损伤情况。
Lastly, the values of long-term dry shrinkage of crushed sand concrete were observed and predicted by using RBF neural network model.
最后，用R BF神经网络模型预测了石屑混凝土的长期干缩值。
The RBF neural network model has better convergence ability and impending speed than the BP neural network model.
RBF神经网络模型的收敛能力和逼近速度优于BP神经网络模型。
RBF Network and SVM model are used to set up the image model of arc sound signal and welding state in order to realize the pattern recognition on-line of the welding conditions.
利用R BF人工神经网络及SVM模型建立电弧声信号与焊接状态的映射模型，从而实现焊接过程状态的模式识别。
Abstract : To effectively evaluate the tracking ability of a photoelectric theodolite, a new tracking error model based on the Radial Basis Function(RBF) neural network was established.
摘要 ： 提出了一种基于径向基函数（RBF）神经网络建立光电经纬仪等效跟踪误差模型的方法来评价光电经纬仪的跟踪性能。
Abstract : To effectively evaluate the tracking ability of a photoelectric theodolite, a new tracking error model based on the Radial Basis Function(RBF) neural network was established.
摘要 ： 提出了一种基于径向基函数（RBF）神经网络建立光电经纬仪等效跟踪误差模型的方法来评价光电经纬仪的跟踪性能。
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