Analysis of the Functional Block Involved in the Design of Radial Basis Function Networks
Neural Processing Letters
Approximation of Gaussian basis functions in the problem of adaptive control of nonlinear objects
Cybernetics and Systems Analysis
Radial basis function neural networks applied to efficient QRST cancellation in atrial fibrillation
Computers in Biology and Medicine
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Radial basis functions for discrimination and regression have been used with some success in a wide variety of applications. Here, we investigate the optimal choice for the form of the basis functions and present an iterative strategy for obtaining the function in a regression context using a conjugate gradient-based algorithm together with a nonparametric smoother. This is developed in a discrimination framework using the concept of optimal scaling. Results are presented for a range of simulated and real data sets