对非线性系统建立T-S模糊模型，并用正交最小二乘法（OLS）对模糊规则的后件参数进行辨识。
T S fuzzy model is constructed for nonlinear system in this paper, and orthogonal least squares (OLS) method is used to identify the parameters of fuzzy ruler consequents.
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。
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.
模糊模型的前件和后件参数分别采用模糊C均值聚类（FCM）和正交最小二乘法（OLS）进行离线或在线辨识。
The T-S fuzzy model's parameters are identified by methods of fuzzy C mean(FCM) and orthogonal least-squares(OLS) online or otherwise.
另外又研究了RBF网络，实现一种叫“正交最小二乘法”的网络训练方法，获得较BP网络快20倍的训练速度，同时语音质量略有提高。
In addition, this paper studies RBF network, realizes the method of orthodoxy least square, gets rapid speed than BP network by 20, and the quality of speech has a little improvement than BP.
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。
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.
提出一种基于正交基函数的小波神经网络设计方法，采用多分辨率学习确定隐含层结构，并用收敛较快的阻尼最小二乘法训练权值。
In this approach the network structure is determined by multiresolution learning, and the weights are trained by damped least squares which has fast convergent rate.
提出一种基于正交基函数的小波神经网络设计方法，采用多分辨率学习确定隐含层结构，并用收敛较快的阻尼最小二乘法训练权值。
In this approach the network structure is determined by multiresolution learning, and the weights are trained by damped least squares which has fast convergent rate.
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