The performance of Binary Artificial Bee Colony (BABC) in structure selection of polynomial NARX and NARMAX Models
This paper explores the capability of the Binary Artificial Bee Colony (BABC) algorithm for feature selection of Nonlinear Autoregressive Moving Average with Exogenous Inputs (NARMAX) model, and compares its implementation with the Binary Particle Swarm Optimization (BPSO) algorithm. A binarized mod...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Insight Society
2017
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| Subjects: | |
| Online Access: | View Fulltext in Publisher View in Scopus |
| Summary: | This paper explores the capability of the Binary Artificial Bee Colony (BABC) algorithm for feature selection of Nonlinear Autoregressive Moving Average with Exogenous Inputs (NARMAX) model, and compares its implementation with the Binary Particle Swarm Optimization (BPSO) algorithm. A binarized modification of the BABC algorithm was used to perform structure selection of the NARMAX model on a Flexible Robot Arm (FRA) dataset. The solution quality and convergence were compared with the BPSO optimization algorithm. Fitting and validation tests were performed using the One-Step Ahead (OSA), correlation and histogram tests. BABC was able to outperform BPSO in terms of convergence consistency with equal solution quality. Additionally, it was discovered that BABC was less prone to converge to local minima while BPSO was able to converge faster. Results from this study showed that BABC was better-suited for structure selection in huge dataset and the convergence has been proven to be more consistent relative to BPSO. |
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| ISBN: | 20885334 (ISSN) |
| ISSN: | 20885334 (ISSN) |
| DOI: | 10.18517/ijaseit.7.2.1387 |