The structure and rational of RBF neural network are presented in detail.
详细地介绍了径向基神经网络的结构和基本原理。
The application of the algorithm in multiuser detection problems demonstrates that the RBF network trained with the algorithm is concise in structure and has good anti-MAI performance.
将该方法用于多用户检测问题的实验结果表明，采用这种混合算法训练的RBF网络结构精简，具有很好的抗多址干扰的性能。
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。
The structure and features of radial basis function (RBF) network are introduced.
介绍了径向基函数(RBF)神经网络的结构和特点。
The structure and principle of Radial Basis Function (RBF) neural network are studied.
分析了径向基函数(RBF)网络的结构和工作原理。
A learning algorithm of subtractive clustering for RBF network is used to obtain the parameters of radial basis function so as to optimize network structure.
在RBF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，使网络结构得到优化。
This paper gives a data fusion structure based on RBF neural network and D-S inference and its application in the fault diagnosis of bearing.
提出一种基于RBF神经网络和D - S证据理论相结合的数据融合结构应用于轴承故障诊断。
Through constructing the RBF network approximation facility, this paper proposes a novel fault diagnosis structure and method applying RBF network to the fault diagnosis of drum level sensor.
本文将基于RBF网络的信息融合技术应用于水位传感器的故障诊断，通过构建高精度RBF 网络逼近器，提出了一种新的故障诊断结构和诊断方法。
The basic structure of evolving knowledge library, the structural principle of RBF network and the procedures of building knowledge library with RBF neural network are presented.
介绍了产生式知识库的基本结构，RBF网络的构成原理及采用RBF神经网络构建知识库的过程。
In this paper, the principle of nonlinear correction of data by using RBF neural network, the structure of neural network and the determination method of parameters are described.
介绍了利用RBF神经网络进行数据非线性校正的原理以及RBF神经网络结构和参数的确定方法。
Radial basis function (RBF) network have unique advantages in control applications due to its features of simple topological structure, quick convergence speed and no local minima.
径向基函数(RBF)神经网络由于其结构简单、收敛速度快、无局部极小等特点使其在控制中的应用有着独特的优势。
Result of traditional RBF network may not be the best, if network structure is defined by empirical formula or the tester before training.
由于传统RBF神经网络在学习前需要通过经验公式或实验者人为确定网络结构，这样训练出的网络往往并非最优。
Result of traditional RBF network may not be the best, if network structure is defined by empirical formula or the tester before training.
由于传统RBF神经网络在学习前需要通过经验公式或实验者人为确定网络结构，这样训练出的网络往往并非最优。
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