Using radial basis functions to approximate a function and its error bounds
- 1 July 1992
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 3 (4) , 624-627
- https://doi.org/10.1109/72.143377
Abstract
A novel network called the validity index network (VI net) is presented. The VI net, derived from radial basis function networks, fits functions and calculates confidence intervals for its predictions, indicating local regions of poor fit and extrapolation.Keywords
This publication has 7 references indexed in Scilit:
- A neural network architecture that computes its own reliabilityComputers & Chemical Engineering, 1992
- Universal Approximation Using Radial-Basis-Function NetworksNeural Computation, 1991
- A neural network approach to statistical pattern classification by 'semiparametric' estimation of probability density functionsIEEE Transactions on Neural Networks, 1991
- Probabilistic neural networksNeural Networks, 1990
- Fast Learning in Networks of Locally-Tuned Processing UnitsNeural Computation, 1989
- An Algorithm for Convex PolytopesJournal of the ACM, 1970
- On Estimation of a Probability Density Function and ModeThe Annals of Mathematical Statistics, 1962