Afterward, its output is estimated by radial basis function neural network (RBFNN) for extracting SEP features.
后级使用径向基神经网络作信号拟合，提取SEP信号的特征。
The main problems in designing a RBFNN depend on fixing the nodes of the hidden layer, the parameters of the centers and the linear weights.
设计中存在的主要问题包括隐层神经元数、中心和半径的确定，以及网络权值的训练。
The paper have studied RBFNN research in deleting redundant attribute emphatically on the basis of analyzing all kinds of neural networks in thesis.
论文在分析各类神经网络的基础上，着重研究了RBFNN在删除冗余属性方面的研究。
A learning algorithm of subtractive clustering method for RBFNN is used to obtain the parameters of radial basis function, so that RBFNN has an optimized structure.
在RBF神经网络中采用了一种减聚类的学习算法来确定径向基函数的相应参数，从而使神经网络结构得到优化。
The weight vector of beamforming is estimated by Doppler information of the signal first, then it is approximated by RBFNN to carry out the blind optimizing beamforming.
该方法首先在不知道任何基阵方向向量先验知识的情况下，利用信号的多普勒信息估计波束形成的权矢量。
The system identification was based on immune strategy RBFNN, and the residuals were generated by on-line comparing the system model outputs with the actual system ones.
系统辨识基于免疫r BF神经网络，用于故障检测的残差是通过对系统的模型输出与系统的实际输出进行在线比较得到的。
The system identification is based on immune strategy RBFNN, and the residuals are generated by on-line comparing the system model outputs with the actual system outputs.
系统辨识是基于免疫RBF神经网络，用于故障检测的残差是通过对系统的模型输出与系统的实际输出的在线比较得到的。
In the algorithm, the radial-basis function neural network (RBFNN) is utilized as forward model, and the IGLSA is used to solve the optimization problem in the inverse problem.
该算法中，径向基函数神经网络(RBFNN)用作前向模型，IGLSA用于求解逆问题中的优化问题。
Through the use of a radial-based function neural network(RBFNN) an intelligent forecasting model for the blended-coal softening temperature was set up under MATLAB environment.
采用径向基神经网络（RBFNN）在MATLAB环境下建立了混煤软化温度的智能预测模型。
This paper firstly analyzes the procurement risk under the EPCM model, and according to the above, proposes a procurement risk assessment model based on TOPSIS and RBFNN method.
本文首先分析了EPCM模式下的采购风险，在此基础上，提出了基于TOPS IS和RBFNN方法来确定采购风险评价模型，并结合工程实例，以一定量的项目采购统计数据进行了实证分析。
Systematic analysis and research are made to the various learning methods of RBFNN. The key factor that influences RBFNN's performance is the choice of RBFNN's hidden layer center.
论文对RBF网络的各种学习算法进行了较系统的分析研究，RBF网络的隐层中心的选择是决定R BF网络性能的最重要的因素。
We advance a new code way in accordance with the RBF neural network, which not only made the whole code process simply and efficient but also accord with the characteristic of RBFNN.
在运用GA的过程中，针对RBF的网络结构提出了与以往不同的编码方式，使得整个编码过程简单有效，而且符合RBF网络本身的特性。
Results show that the RBFNN is obviously superior to the traditional linear model, and its MAE (mean absolute error) and RMSE (root mean square error) are 41.8 and 55.7, respectively.
结果显示，该模型预测效果明显优于传统的线性自回归预测模型，各月平均的平均绝对误差（MAE）和均方误差（RMSE）达到41.8和55.7。
The result verifies that the hybrid modeling. incorporating the merits of RBFNN and principle modeling, has great advantage, and can be a feasible way to improve the modeling accuracy.
研究表明，结合两者优势的混合模型具有很大的优越性，是提高建模精度的可行途径。
The experimental results on the real industrial data demonstrate that the model based on SVM achieves good performance and has less prediction errors than those of BPNN and RBFNN models.
在实际工业数据上进行的实验结果表明，支持向量机模型对丙酮纯度具有良好的预测效果，性能优于反向传播神经网络和径向基网络模型。
The principle model is the main part of the serial hybrid model, RBFNN models the mathematical relation between two parameters of system, which can not be expressed exactly by principles.
串行互补模型以机理模型为主，RBF神经网络描述变量间难以用机理精确表示的函数关系。
In the view of characteristics of basic model, the article gives RBFNN model for system measurement as supplement to the former, the result of test for RBFNN approves that the model is effective.
鉴于基本模型自身的特点，作为对它的补充，文中又提出了系统测量的RBFNN模型，系统测试的结果证实了这种模型的有效性。
The method of radial basis function neural network (RBFNN) is given to correct the nonlinear errors of the sensors. A BP neural network has been developed to solve the same problem for comparison.
提出了传感器非线性误差校正的径向基函数(RBF)神经网络方法，并与采用BP神经网络校正非线性误差进行了比较。
In this paper, the stop condition for recursion orthogonal least square (ROLS) algorithm is improved, and the optimal number of hidden neurons in RBFNN is chosen using this improved ROLS algorithm.
本文改进了递归正交最小二乘（ROLS）算法的停止条件，并用改进的ROLS算法优选RBF神经网络中隐单元的个数；
In this paper, the stop condition for recursion orthogonal least square (ROLS) algorithm is improved, and the optimal number of hidden neurons in RBFNN is chosen using this improved ROLS algorithm.
本文改进了递归正交最小二乘（ROLS）算法的停止条件，并用改进的ROLS算法优选RBF神经网络中隐单元的个数；
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