A recursive least squares parameter estimation algorithm for output nonlinear autoregressive systems using the input–output data filtering

Nonlinear systems exist widely in industrial processes. This paper studies the parameter estimation methods of establishing the mathematical models for a class of output nonlinear systems, whose output is nonlinear about the past outputs and linear about the inputs. We use an estimated noise transfe...

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Bibliographic Details
Published in:Journal of the Franklin Institute Vol. 354; no. 15; pp. 6938 - 6955
Main Authors: Ding, Feng, Wang, Yanjiao, Dai, Jiyang, Li, Qishen, Chen, Qijia
Format: Journal Article
Language:English
Published: Elmsford Elsevier Ltd 01.10.2017
Elsevier Science Ltd
Subjects:
Algorithms
Computer simulation
Filtration
Least squares method
Mathematical models
Noise
Nonlinear systems
Parameter estimation
Parameter identification
Parameters
Simulation
Transfer functions
ISSN:0016-0032, 1879-2693, 0016-0032
Online Access:Get full text
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Summary:Nonlinear systems exist widely in industrial processes. This paper studies the parameter estimation methods of establishing the mathematical models for a class of output nonlinear systems, whose output is nonlinear about the past outputs and linear about the inputs. We use an estimated noise transfer function to filter the input–output data and obtain two identification models, one containing the parameters of the system model, and the other containing the parameters of the noise model. Based on the data filtering technique, a data filtering based recursive least squares algorithm is proposed. The simulation results show that the proposed algorithm can generate more accurate parameter estimates than the recursive generalized least squares algorithm.
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content type line 14
ISSN:0016-0032
1879-2693
0016-0032
DOI:10.1016/j.jfranklin.2017.08.009