Approximating networks and extended Ritz method for the solution of functional optimization problems
Journal of Optimization Theory and Applications
Fault diagnosis for nonlinear systems using a bank of neural estimators
Computers in Industry - Special issue: Soft computing in industrial applications
On the tractability of multivariate integration and approximation by neural networks
Journal of Complexity
Learning with generalization capability by kernal methods of bounded complexity
Journal of Complexity
Journal of Approximation Theory
Estimates of covering numbers of convex sets with slowly decaying orthogonal subsets
Discrete Applied Mathematics
Complexity of Gaussian-radial-basis networks approximating smooth functions
Journal of Complexity
Accuracy of suboptimal solutions to kernel principal component analysis
Computational Optimization and Applications
Learning with generalization capability by kernel methods of bounded complexity
Journal of Complexity
Output feedback sliding mode control with support vector machine based observer gain adaptation
CSECS'09 Proceedings of the 8th WSEAS International Conference on Circuits, systems, electronics, control & signal processing
WSEAS Transactions on Systems and Control
Computational Optimization and Applications
A neural network of smooth hinge functions
IEEE Transactions on Neural Networks
Some comparisons of model complexity in linear and neural-network approximation
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Approximation bound of mixture networks in Lwp spaces
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Book review: Adaptive neural network control of robotic manipulators
Automatica (Journal of IFAC)
Suboptimal Solutions to Team Optimization Problems with Stochastic Information Structure
SIAM Journal on Optimization
Regularized vector field learning with sparse approximation for mismatch removal
Pattern Recognition
Hi-index | 754.84 |
Sets of multivariable functions are described for which worst case errors in linear approximation are larger than those in approximation by neural networks. A theoretical framework for such a description is developed in the context of nonlinear approximation by fixed versus variable basis functions. Comparisons of approximation rates are formulated in terms of certain norms tailored to sets of basis functions. The results are applied to perceptron networks