Partially Coupled Stochastic Gradient Identification Methods for Non-Uniformly Sampled Systems

This technical note addresses identification problems of non-uniformly sampled systems. For the input-output representation of non-uniform discrete-time systems, a partially coupled stochastic gradient (C-SG) algorithm is proposed to estimate the model parameters with high computational efficiency c...

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Bibliographic Details
Published in:IEEE transactions on automatic control 2010-08, Vol.55 (8), p.1976-1981
Main Authors: Feng Ding, Guangjun Liu, Liu, Xiaoping Peter
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Automatic control
Chemical sensors
Computational efficiency
Computer science
control theory
systems
Control systems
Control theory. Systems
Discretization
Estimates
Exact sciences and technology
Infinity
Mathematical models
Modelling and identification
multirate systems
non-uniform sampling
Parameter estimation
Predictive control
Process control
recursive identification
Representations
Sampling methods
Sensor systems
State estimation
State-space methods
stochastic gradient
Stochastic systems
Stochasticity
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Summary:This technical note addresses identification problems of non-uniformly sampled systems. For the input-output representation of non-uniform discrete-time systems, a partially coupled stochastic gradient (C-SG) algorithm is proposed to estimate the model parameters with high computational efficiency compared with the standard stochastic gradient (SG) algorithm. The analysis indicates that the partially C-SG algorithm can give more accurate parameter estimates than the SG algorithm. The parameter estimates obtained using the partially C-SG algorithm converge to their true values as the data length approaches infinity.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2010.2050713