Name
Title | ||
|---|---|---|
Using unscented kalman filter for training the minimal resource allocation neural network |
Abstract | ||
|---|---|---|
The MARN has the same structure as the RBF network and has the ability to grow and prune the hidden neurons to realize a minimal network structure. Several algorithms have been used to training the network. This paper proposes the use of Unscented Kalman Filter (UKF) for training the MRAN parameters i.e. centers, radii and weights of all the hidden neurons. In our simulation, we implemented the MRAN trained with UKF and the MRAN trained with EKF for states estimation. It is shown that the MRAN trained with UKF is superior than the MRAN trained with EKF. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11539087_2 | ICNC (1) |
Keywords | Field | DocType |
unscented kalman filter,hidden neuron,minimal network structure,states estimation,mran parameter,minimal resource allocation neural,rbf network,resource allocation,neural network | Extended Kalman filter,Radial basis function,Computer science,Kalman filter,Resource allocation,Artificial intelligence,Artificial neural network,Machine learning,Network structure | Conference |
Volume | ISSN | ISBN |
3610 | 0302-9743 | 3-540-28323-4 |
Citations | PageRank | References |
1 | 0.38 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ye Zhang | 1 | 304 | 43.70 |
| Yiqiang Wu | 2 | 4 | 2.47 |
| Wenquan Zhang | 3 | 1 | 1.74 |
| Yi Zheng | 4 | 1 | 0.38 |