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Practical identification of NARMAX models using radial basis functions
Abstract
A wide class of discrete-time non-linear systems can be represented by the nonlinear autoregressive moving average (NARMAX) model with exogenous inputs. This paper develops a practical algorithm for identifying NARMAX models based on radial basis functions from noise-corrupted data. The algorithm consists of an iterative orthogonal-forward-regression routine coupled with model validity tests. The orthogonal-forward-regression routine selects parsimonious radial-basisTunc-tion models, while the model validity tests measure the quality of fit. The modelling of a liquid level system and an automotive diesel engine are included to demonstrate the effectiveness of the identification procedure.
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Cites
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Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks
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Self-citationInterpolation of Scattered Data: Distance Matrices and Conditionally Positive Definite Functions
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Self-citationRepresentations of non-linear systems: the NARMAX model
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Cited by
Self-constructing wavelet neural network algorithm for nonlinear control of large structures
Wang, N., & Adeli, H. (2015) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2015.01.018 · Cited by 103
Metadata
- Authors
- CHEN, S., BILLINGS, S. A., COWAN, C. F. N., & GRANT, P. M.
- Venue
- International Journal of Control
- Publisher
- Informa UK Limited
- Published
- 1990-12
- Volume
- 52
- Issue
- 6
- Pages
- 1327-1350
- ISSN
- 0020-7179 (ISSN-L)
- 1366-5820
- OpenAlex
- W1975100741
- Cited by
- 306
- References
- 20
- FWCI
- 17.88
- Type
- journal article
- Language
- en
- Open access
- closed
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- CHEN, S., BILLINGS, S. A., COWAN, C. F. N., & GRANT, P. M. (1990). Practical identification of NARMAX models using radial basis functions. International Journal of Control. https://doi.org/10.1080/00207179008953599
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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