|Maximum Likelihood-based Multi-innovation Stochastic Gradient Method for Multivariable Systems
Huafeng Xia*, Yan Ji, Yanjun Liu, and Ling Xu
International Journal of Control, Automation, and Systems, vol. 17, no. 3, pp.565-574, 2019
Abstract : "This paper considers the parameter estimation problems for multivariable controlled autoregressive moving
average systems. By means of the decomposition technique, a multivariable system is transformed into several
identification submodels according to the number of outputs. A maximum likelihood extended stochastic gradient
identification algorithm is derived for identifying each subsystem by using the maximum likelihood principle. In
order to improve the convergence rate, a multivariable maximum likelihood-based muti-innovation stochastic gradient
algorithm is proposed. The proposed algorithms can generate more accurate parameter estimates compared with
the multivariable extended stochastic gradient algorithm. The illustrative simulation results show that the proposed
methods work well."
Maximum likelihood, multi-innovation identification theory, multivariable system, parameter estimation, stochastic gradient.