Universal approximation using radial-basis-function networks
Neural Computation
Neural Computation
Automatica (Journal of IFAC)
Fuzzy Systems as Universal Approximators
IEEE Transactions on Computers
Regularization theory and neural networks architectures
Neural Computation
Regularized neural networks: some convergence rate results
Neural Computation
An equivalence between sparse approximation and support vector machines
Neural Computation
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Fuzzy systems with overlapping Gaussian concepts: approximation properties in Sobolev norms
Fuzzy Sets and Systems - Fuzzy models
Fuzzy piecewise multilinear and piecewise linear systems as universal approximators in Sobolev norms
IEEE Transactions on Fuzzy Systems
Approximation theory of fuzzy systems-SISO case
IEEE Transactions on Fuzzy Systems
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In this paper we propose a new nonparametric regression algorithm based on fuzzy systems with overlapping concepts. We analyze its consistency properties, showing that it is capable to reconstruct an infinite-dimensional class of function when the size of the noisy dataset grows to infinity. Moreover, convergence to the target function is guaranteed in Sobolev norms so ensuring uniform convergence also for a certain number of derivatives. The connection with regularization networks, Bayesian estimation and Tychonov regularization is highlighted.