The mathematical models of coaxality error by evaluation of the orthogonal least square method and the minimum condition method are presented in this paper.
本文给出了同轴度误差正交最小二乘评定与最小条件评定法的数学模型。
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 premise structure of fuzzy model is confirmed by the improved fuzzy clustering, and fuzzy relation matrix of fuzzy model is confirmed by orthogonal least square.
通过改进模糊聚类方法确定模糊模型的前件结构，并对模糊推理关系矩阵进行正交最小二乘估计。
In addition, the conclusion parameters of fuzzy model are confirmed by the orthogonal least square in order to optimize the structure and the parameters of fuzzy model.
另外，再次通过正交最小二乘方法确定模糊模型的结论参数，实现模糊模型结构和参数的优化。
Combining the new measure with the forward regression orthogonal least square (OLS), not only the parameters of the classification hyperplane, but also the important input nodes can be obtaind.
提出一种基于输入集分类函数的新的距离度量方法，它与前传回归的正交最小二乘法相结合，不仅可以学习分类超平面的参数，而且可以选择重要的输入节点。
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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