In the RBF network, to overcome the defects of traditional K-means scheme with local search, an orthogonal least square algorithm is used to select RBF center.
在RBF网络中，为了克服传统K均值聚类法局部寻优的缺陷，采用了正交最小二乘法选取rBF中心。
The model USES an improved nearest-neighbor clustering algorithm to select the RBF center, and a recursive least square algorithm to train weights of the RBF neural network.
该模型首先采用改进的最近邻聚类算法确定径向基函数中心，接着应用递推最小二乘法训练网络的权值。
A recursive least squares algorithm for linking weight between linear layers of RBF network is introduced, and a dynamic recursive algorithm of center vector is proposed.
介绍了RBF网络线性层权值的训练算法——递推最小二乘法，及中心向量的动态递推算法。
The RBF network configuration is formulated as a minimization problem with respect to the number of hidden layer nodes, the center locations and the connection weights.
R BF网络的设计问题就是关于网络隐节点数和隐层节点RBF函数中心、宽度和隐层到输出层的权值的性能指标的最小化问题。
The choice of the center of radial basis function, constructing an improved RBF network and its application to recognize the trained samples and test samples were discussed.
讨论了径向基函数中心的选取，构造了改进的RBF网络对训练样本和测试样本进行识别。
A neural network based on radial basis function (RBF) was used to predict and compensate the thermal error of a CNC turning center.
文中使用径向基函数理论建立了基于RBF神经网络的数控机床热误差数学模型。
A neural network based on radial basis function (RBF) was used to predict and compensate the thermal error of a CNC turning center.
文中使用径向基函数理论建立了基于RBF神经网络的数控机床热误差数学模型。
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