Loading...
Please wait, while we are loading the content...
Similar Documents
Neural network representation and learning of mappings and their derivatives
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Gallant, A. Ronald White, Halbert Hornik, Kurt Stinchcombe, Maxwell |
| Copyright Year | 1991 |
| Description | Discussed here are recent theorems proving that artificial neural networks are capable of approximating an arbitrary mapping and its derivatives as accurately as desired. This fact forms the basis for further results establishing the learnability of the desired approximations, using results from non-parametric statistics. These results have potential applications in robotics, chaotic dynamics, control, and sensitivity analysis. An example involving learning the transfer function and its derivatives for a chaotic map is discussed. |
| File Size | 540282 |
| Page Count | 36 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_19910012467 |
| Archival Resource Key | ark:/13960/t19k97t0s |
| Language | English |
| Publisher Date | 1991-02-01 |
| Access Restriction | Open |
| Subject Keyword | Cybernetics Controllers Theorems Sensitivity Operators Mathematics Neural Nets Transfer Functions Derivation Control Theory Robotics Matrices Mathematics Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Article |
