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      On the construction and training of reformulated radial basis function neural networks

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      IEEE Transactions on Neural Networks
      Institute of Electrical and Electronics Engineers (IEEE)

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          Fast Learning in Networks of Locally-Tuned Processing Units

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            Orthogonal least squares learning algorithm for radial basis function networks.

            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 authors propose 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. In the algorithm, 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. This is illustrated using examples taken from two different signal processing applications.
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              Interpolation of scattered data: Distance matrices and conditionally positive definite functions

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                Author and article information

                Journal
                IEEE Transactions on Neural Networks
                IEEE Trans. Neural Netw.
                Institute of Electrical and Electronics Engineers (IEEE)
                1045-9227
                July 2003
                July 2003
                : 14
                : 4
                : 835-846
                Article
                10.1109/TNN.2003.813841
                593c8c7a-f71f-432f-b33f-bea62b72f811
                © 2003
                History

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