Approximating I/O data using Radial Basis Functions: A new clustering-based approach
Computational Intelligence and Bioinspired Systems: 8th International Work …, 2005•Springer
In this paper, we deal with the problem of function approximation from a given set of
input/output data. This problem consists of analyzing these training examples so that we can
predict the output of the model given new inputs. We present a new method for function
approximation of the I/O data using radial basis functions (RBFs). This approach is based on
a new efficient method of clustering of the centres of the RBF Network (RBFN); it uses the
objective output of the RBFN to move the clusters instead of just the input values of the I/O …
input/output data. This problem consists of analyzing these training examples so that we can
predict the output of the model given new inputs. We present a new method for function
approximation of the I/O data using radial basis functions (RBFs). This approach is based on
a new efficient method of clustering of the centres of the RBF Network (RBFN); it uses the
objective output of the RBFN to move the clusters instead of just the input values of the I/O …
Abstract
In this paper, we deal with the problem of function approximation from a given set of input/output data. This problem consists of analyzing these training examples so that we can predict the output of the model given new inputs. We present a new method for function approximation of the I/O data using radial basis functions (RBFs). This approach is based on a new efficient method of clustering of the centres of the RBF Network (RBFN); it uses the objective output of the RBFN to move the clusters instead of just the input values of the I/O data. This method of clustering, especially designed for function approximation problems, improves the performance of the approximator system obtained, compared with other models derived from traditional algorithms.
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