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r BF中心。

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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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