ORTHOGONAL LEAST-SQUARES LEARNING ALGORITHM FOR RADIAL BASIS FUNCTION NETWORKS
ORTHOGONAL LEAST-SQUARES LEARNING ALGORITHM FOR RADIAL BASIS FUNCTION NETWORKS
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DOI：
10.1109/72.80341
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发表时间：
1991-03-01
影响因子：
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通讯作者：
GRANT, PM
中科院分区：
文献类型：
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作者：
CHEN, S;COWAN, CFN;GRANT, PM
在许多信号处理应用中，径向基函数网络为两层神经网络提供了可行的替代方案。 用于径向基函数网络的常见学习算法是基于首先选择一些数据点作为径向基函数中心，然后使用单数值分解来求解网络的权重。 这样的过程有几个缺点，尤其是任意选择中心的选择显然不令人满意。 本文提出了一种基于正交最小二乘法的替代学习程序。 该过程选择径向基函数以合理的方式一一以一个为单位的中心，直到构建了足够的网络为止。 该算法具有每个选定的中心的属性，最大程度地提高了所需输出的解释方差或能量的增量，并且不会遭受数值不良条件问题。 正交最小二乘学习策略提供了一种简单有效的手段，用于拟合径向基函数网络，并使用从两个不同的信号处理应用程序中获取的示例来说明这一点。
The radial basis function network offers a viable alternative to the two-layer neural network in many applications of signal processing. A common learning algorithm for radial basis function networks is based on first choosing randomly some data points as radial basis function centers and then using singular value decomposition to solve for the weights of the network. Such a procedure has several drawbacks and, in particular, an arbitrary selection of centers is clearly unsatisfactory. The paper proposes an alternative learning procedure based on the orthogonal least squares method. The procedure chooses radial basis function centers one by one in a rational way until an adequate network has been constructed. The algorithm has the property that each selected center maximizes the increment to the explained variance or energy of the desired output and does not suffer numerical ill-conditioning problems. The orthogonal least squares learning strategy provides a simple and efficient means for fitting radial basis function networks, and this is illustrated using examples taken from two different signal processing applications.