Nonlinear System Identification Using Temporal Convolutional Networks: A Silverbox Study
| Autor: | Keith Redmill, Umit Ozguner, John M. Maroli |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Nonlinear system identification Artificial neural network Computer science 020208 electrical & electronic engineering 02 engineering and technology computer.software_genre Damper Nonlinear system Identification (information) 020901 industrial engineering & automation Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Benchmark (computing) Feedforward neural network Data mining computer |
| Zdroj: | IFAC-PapersOnLine. 52:186-191 |
| ISSN: | 2405-8963 |
| Popis: | Identification of nonlinear systems is presented using a neural network variant known as the temporal convolutional network (TCN). The identification capabilities of TCNs and standard feedforward neural networks (FNNs) are benchmarked and compared using the Silverbox dataset: a publicly available dataset from a circuit equivalent to a nonlinear spring-mass damper. The TCN is found to have superior performance in simulation of the test portion of the dataset. In addition, published benchmark results are surveyed and compared to the TCN results. Analysis of existing results reveals testing variances that effect model performance, so guidelines for fair comparison of models on the Silverbox benchmark are presented. |
| Databáze: | OpenAIRE |
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