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where the approximating function <math display="inline">y(\mathbf{x})</math> is represented as a sum of <math>N</math> radial basis functions, each associated with a different center <math display="inline">\mathbf{x}_i</math>, and weighted by an appropriate coefficient <math display="inline">w_i.</math> The weights <math display="inline">w_i</math> can be estimated using the matrix methods of [[Weighted least squares|linear least squares]], because the approximating function is ''linear'' in the weights ''<math display="inline">w_i</math>''.
Approximation schemes of this kind have been particularly used{{citation needed|date=July 2013}} in [[time series prediction]] and [[Control theory|control]] of [[nonlinear systems]] exhibiting sufficiently simple [[chaos theory|chaotic]] behaviour
==RBF Network==
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