Particle filtering based parameter estimation for systems with output-error type model structures

The output-error model structure is often used in practice and its identification is important for analysis of output-error type systems. This paper considers the parameter identification of linear and nonlinear output-error models. A particle filter which approximates the posterior probability dens...

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Bibliographic Details
Published inJournal of the Franklin Institute Vol. 356; no. 10; pp. 5521 - 5540
Main Authors Ding, Jie, Chen, Jiazhong, Lin, Jinxing, Wan, Lijuan
Format Journal Article
LanguageEnglish
Published Elmsford Elsevier Ltd 01.07.2019
Elsevier Science Ltd
Subjects
Algorithms
Computer simulation
Conditional probability
Convergence
Error analysis
Innovations
Mathematical models
Nonlinear systems
Parameter estimation
Parameter identification
Probability density functions
Random sampling
Simulation
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Summary:The output-error model structure is often used in practice and its identification is important for analysis of output-error type systems. This paper considers the parameter identification of linear and nonlinear output-error models. A particle filter which approximates the posterior probability density function with a weighted set of discrete random sampling points is utilized to estimate the unmeasurable true process outputs. To improve the convergence rate of the proposed algorithm, the scalar innovations are grouped into an innovation vector, thus more past information can be utilized. The convergence analysis shows that the parameter estimates can converge to their true values. Finally, both linear and nonlinear results are verified by numerical simulation and engineering.
ISSN:0016-0032
1879-2693
0016-0032
DOI:10.1016/j.jfranklin.2019.04.027