Projection-based gradient descent training of radial basis function networks
Projection-based gradient descent training of radial basis function networks
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DOI：
10.1109/ijcnn.2004.1380131
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发表时间：
2004-07
期刊：
影响因子：
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通讯作者：
M. K. Muezzinoglu;J. Zurada
中科院分区：
文献类型：
--
作者：
M. K. Muezzinoglu;J. Zurada
提出了一种新的径向基函数（RBF）网络训练过程，该过程在参数搜索中采用线性投影技术。为了与传统的中心和/或权重调整方法同时应用，引入了 RBF 单元宽度参数的梯度下降迭代。该过程使用的投影机制避免了负宽度参数，并能够检测冗余单元，然后可以从网络中修剪掉这些冗余单元。所提出的训练方法用于设计非线性设备的反馈神经控制器，以跟踪所需的轨迹。
A new radial basis function (RBF) network training procedure that employs a linear projection technique along parameter search is proposed. To be applied simultaneously with the conventional center and/or weight adjustment methods, a gradient descent iteration on the width parameters of RBF units is introduced. The projection mechanism used by the procedure avoids negative width parameters and enables detection of redundant units, which can then be pruned from the network. Proposed training approach is applied to design a feedback neuro-controller for a nonlinear plant to track a desired trajectory.