A learning algorithm of subtractive clustering method for RBFNN is used to obtain the parameters of radial basis function, so that RBFNN has an optimized structure.
在RBF神经网络中采用了一种减聚类的学习算法来确定径向基函数的相应参数，从而使神经网络结构得到优化。
A new method of subtractive clustering RBF network for air condition breeze fan fault diagnosis is presented.
提出了一种减聚类径向基函数神经网络的纺织空调送风风机故障诊断方法。
First, determining the initial system by the method of subtractive clustering. Second, learning the system.
首先，通过减法聚类来确定初始系统，然后再进行学习训练。
In this method, subtractive clustering was adopted to divide the input space into several sub-spaces, and sub-models were built by Least Square SVM (ls SVM) in every sub-space.
该建模方法通过减聚类方法将输入空间划分为一些小的局部空间，在每个局部空间中用最小二乘支持向量机(LS -SVM)建立子模型。
In this method, subtractive clustering was adopted to divide the input space into several sub-spaces, and sub-models were built by Least Square SVM (ls SVM) in every sub-space.
该建模方法通过减聚类方法将输入空间划分为一些小的局部空间，在每个局部空间中用最小二乘支持向量机(LS -SVM)建立子模型。
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