By combining the coupling identification concept with the gradient search, this study develops a partially coupled generalised extended projection algorithm and a partially coupled generalised extended stochastic grad...
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By combining the coupling identification concept with the gradient search, this study develops a partially coupled generalised extended projection algorithm and a partially coupled generalised extended stochastic gradient algorithm to estimate the parameters of a multivariable output-error-like system with autoregressive moving average noise from input-output data. The key is to divide the identification model into several submodels based on the hierarchical identification principle and to establish the parameter estimation algorithm by using the coupled relationship between these submodels. The simulation test results indicate that the proposed algorithms are effective.
This study considers the parameter estimation of a multi-variable output-error-likesystem with autoregressive moving average noise. In order to solve the problem of the information vector containing unknown variables...
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This study considers the parameter estimation of a multi-variable output-error-likesystem with autoregressive moving average noise. In order to solve the problem of the information vector containing unknown variables, a least squares-based iterative algorithm is proposed by using the iterative search. The original system is divided into several subsystems by using the decomposition technique. However, the subsystems contain the same parameter vector, which poses a challenge for the identification problem, the approach taken here is to use the coupling identification concept to cut down the redundant parameter estimates. In addition, the recursive least squares algorithm is provided for comparison. The simulation results indicate that the proposed algorithms are effective.
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