Universal approximation using radial-basis-function networks
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
Fuzzy piecewise multilinear and piecewise linear systems as universal approximators in Sobolev norms
IEEE Transactions on Fuzzy Systems
Approximation theory of fuzzy systems-MIMO case
IEEE Transactions on Fuzzy Systems
Spline approximation of fuzzy functions
MATH'05 Proceedings of the 8th WSEAS International Conference on Applied Mathematics
Consistent Sobolev regression via fuzzy systems with overlapping concepts
Fuzzy Sets and Systems
| Hi-index | 0.00 |
In this paper the approximating capabilities of fuzzy systems with overlapping Gaussian concepts are considered. The target function is assumed to be sampled either on a regular gird or according to a uniform probability density. By exploiting a connection with Radial Basis Functions approximators, a new method for the computation of the system coefficients is provided, showing that it guarantees uniform approximation of the derivatives of the target function.