Effect of swarm size parameter on Binary Particle Swarm optimization-based NARX structure selection
The NARX identification process is performed in two steps, namely model structure selection and parameter estimation. Structure selection involves selecting a subset of regressors to use that best describes the system. A Binary Particle Swarm based (BPSO) structure selected method has been implement...
| Published in: | ISIEA 2012 - 2012 IEEE Symposium on Industrial Electronics and Applications |
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| Main Author: | |
| Format: | Conference paper |
| Language: | English |
| Published: |
2012
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84876766718&doi=10.1109%2fISIEA.2012.6496632&partnerID=40&md5=8675bfaf88e522bb84b9e469009615ae |
| Summary: | The NARX identification process is performed in two steps, namely model structure selection and parameter estimation. Structure selection involves selecting a subset of regressors to use that best describes the system. A Binary Particle Swarm based (BPSO) structure selected method has been implemented previously. The BPSO algorithm is subject to several parameters, namely swarm size, maximum iterations and its initial positions. This paper investigates the effect of the swarm size parameter on the convergence of the algorithm. Experiments were conducted on the DC motor dataset. The results indicate that the optimal swarm size for convergence was between 20 to 30 particles. © 2012 IEEE. |
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| DOI: | 10.1109/ISIEA.2012.6496632 |