6
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Using wavelet network in nonparametric estimation

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Related collections

          Most cited references25

          • Record: found
          • Abstract: not found
          • Article: not found

          Multilayer feedforward networks are universal approximators

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Universal approximation bounds for superpositions of a sigmoidal function

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              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.
                Bookmark

                Author and article information

                Journal
                IEEE Transactions on Neural Networks
                IEEE Trans. Neural Netw.
                Institute of Electrical and Electronics Engineers (IEEE)
                10459227
                March 1997
                : 8
                : 2
                : 227-236
                Article
                10.1109/72.557660
                0719e432-e5b9-443c-80ea-9056e6f6dfb5
                History

                Comments

                Comment on this article