Embedded radial basis function networks to compensate for modeling uncertainty of nonlinear dynamic systems
This thesis provides a bridge between analytical modeling and neural network modeling. Two different approaches have been explored. Both approaches rely on embedding radial basis function (RBF) modules in the approximate model of the plant so that they can be trained to compensate for modeling uncer...
| Main Author: | |
|---|---|
| Language: | ENG |
| Published: |
ScholarWorks@UMass Amherst
2000
|
| Subjects: | |
| Online Access: | https://scholarworks.umass.edu/dissertations/AAI9960753 |