Stochastic global optimization methods. part 11: multi level methods
Mathematical Programming: Series A and B
Multilayer feedforward networks are universal approximators
Neural Networks
A tolerant algorithm for linearly constrained optimization calculations
Mathematical Programming: Series A and B
Numerical algorithms with Fortran
Numerical algorithms with Fortran
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Artificial neural networks for solving ordinary and partial differential equations
IEEE Transactions on Neural Networks
Neural-network methods for boundary value problems with irregular boundaries
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
We introduce the Neural Spline, that is a mathematical model built by combining a neural network and an associated Obreshkov polynomial. The neural spline has finite support and can be used as the basic element in constructing continuous modular neural-based models. These models are suitable for function approximation in partitioned domains and are also amenable to efficient parallel or distributed implementation. Experimental results are presented for test problems in one and two dimensions which illustrate the effectiveness of the proposed function approximation scheme.