Name
Title | ||
|---|---|---|
Convolutional Neural Networks as Asymmetric Volterra Models Based on Generalized Orthonormal Basis Functions. |
Abstract | ||
|---|---|---|
This paper introduces a convolutional neural network (CNN) approach to derive Volterra models of dynamical systems based on generalized orthonormal basis function (GOBF)-Volterra. The approach derives the parameters of the model through a CNN and the neural network’s learned weights represent the poles of a system. Simulation results show that the parameters of the system can be exactly recovered when no noise is applied. Furthermore, when noise is present, the errors in the parameters are very small for both the linear and nonlinear cases. Finally, the approach is used to identify the model of a quadcopter using data from actual flight tests. Comparisons with previous works demonstrate that CNNs can be satisfactorily used for the identification of dynamical systems. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TNNLS.2019.2911603 | IEEE transactions on neural networks and learning systems |
Keywords | Field | DocType |
Kernel,Mathematical model,Convolution,Convolutional neural networks,Nonlinear systems,Computational modeling | Pattern recognition,Computer science,Convolutional neural network,Artificial intelligence,Orthonormal basis functions | Journal |
Volume | Issue | ISSN |
31 | 3 | 2162-237X |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeremias B. Machado | 1 | 21 | 2.70 |
| Sidney Nascimento Givigi | 2 | 64 | 12.40 |