1) radial-basis network
径基网络
3) radial basis function network
径向基网络
1.
The radial basis function network (RBFN) is studied and its neurocomputing performance is contrasted with fuzzy logic operation.
对径向基网络的功能进行了研究，并对该网络的神经计算功能与模糊逻辑运算进行了对比分析，得出了以下结论：径向基网络具有模糊逻辑运算的性质，从某种意义讲，径向基网络是一种神经元模糊系统。
2.
A novel pattern recognition method based on wavelet packet analysis and radial basis function network is presented in this paper.
提出的小波包分析与径向基网络相结合的方法, 可以很好地解决这个难题。
3.
This paper proposes an organizing radial basis function network with a hybrid learning algorithm.
论文给出了改进型径向基网络应用示例,验证了改进型径向基网络的函数实现功能和模式分类功能。
4) radial basis function neural network
径向基网络
1.
An approach of Radial Basis Function Neural Network(RBF NN) optimization based on support vector machine was proposed to solve the randomness of the network structure and the unstableness of the network’s performance.
为了解决径向基网络(RBFNN)结构设计的随机性,进一步优化RBF网络性能,提出一种基于支持向量机(SVM)的径向基网络结构优化方法。
5) RBF network
径向基网络
1.
Face recognition based on independent component analysis and RBF network;
基于独立分量分析和径向基网络的人脸识别方法
2.
The RBF network was employed as a quantified model to assess the customer satisfaction where an algorithm was designed to determine related parameters.
分析航运企业经营实践,建立了航运企业顾客满意度测评指标体系,利用径向基网络模型进行测评,设计了一种参数选择算法,通过网络训练、仿真与测试,得出了顾客满意度测评结果。
3.
The hidden layer of RBF network was designed dynamically by virtue of the modified growing neural gas algorithm so as to realize the adaptive understanding of the continuous state space.
针对连续状态与动作空间下的控制问题,提出了一类连续状态与动作空间下的加权Q学习算法,应用改进的增长神经气算法动态构建径向基网络的隐含层,实现状态空间的自适应构建。
6) RBF neural network
径向基网络
1.
Safety Evalation for the Concrete Filled Steel Tube Arch Bridge Based on RBF Neural Network;
基于径向基网络的钢管混凝土拱桥安全性评价
2.
With consideration of several key factors which affect the whole safety state such as load bearing capability,damage of bearing component and damaged state of appearance,RBF neural network models were built and trained by checking samples gained from the data measured on the spot.
以全桥安全性评价为总体目标,在以往桥梁安全性评价方法的基础上,引入人工神经网络理论,并结合层次分析法,提出了基于径向基网络的钢管混凝土拱桥安全性评价方法。
3.
RBF Neural Network Identification for Inner-Rings Gas Pressure Based on Cylinder Head Vibration Signal;
结果表明:利用径向基网络和ARMA时间序列分析法,均能较为准确地识别活塞环环腔气体压力和气缸内气体燃烧压力;径向基神经网络的识别方法比ARMA时间序列识别方法更加准确。
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