Paul Yee

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| In this paper we exploit the one-to-one correspondences between the recursive least-squares (RLS) and Kalman variables to formulate extended forms of the RLS algorithm. Two particular forms of the extended RLS algorithm are considered, one pertaining to a system identiication problem and the other pertaining to the tracking of a chirped sinusoid in(More)
—In this paper, constructive approximation theorems are given which show that under certain conditions, the standard Nadaraya-Watson regression estimate (NWRE) can be considered a specially regularized form of radial basis function networks (RBFN's). From this and another related result, we deduce that regularized RBFN's are m.s. consistent, like the NWRE(More)
Pattern classiication may be viewed as an ill-posed, inverse problem to which the method of regularization be applied. In doing so, a proper theoretical framework is provided for the application of radial basis function (RBF) networks to pattern classiication, with strong links to the classical kernel regression estimator (KRE)-based classiiers that(More)
A dynamic network of regularized Gaussian radial basis functions (GaRBF) is described for the one-step prediction of nonlinear, nonstationary autoregressive (NLAR) processes governed by a smooth process map and a zero-mean, independent additive disturbance process of bounded variance. For N basis functions, both full-order and reduced-order updating(More)
In this paper, constructive approximation theorems are given which show that, under certain conditions, the standard Nadaraya-Watson regression estimate (NWRE) can be considered a specially regularized form of radial basis function networks (RBFNs). From this and another related result, we deduce that regularized RBFNs are m.s. consistent, like the NWRE,(More)
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