Auxiliary Model‐Based Maximum Likelihood Multi‐Innovation Forgetting Gradient Identification for a Class of Multivariable Systems
Huihui Wang, Ximei Liu
Abstract
ABSTRACT Through dividing a multivariable system into several subsystems, this paper derives the sub‐identification model. Utilizing the obtained sub‐identification model, an auxiliary model‐based maximum likelihood forgetting gradient algorithm is derived. Considering enhancing the parameter estimation accuracy, the auxiliary model‐based maximum likelihood multi‐innovation forgetting gradient (AM‐ML‐MIFG) algorithm is proposed taking advantage of the multi‐innovation identification theory. Simulation results test the effectiveness of the proposed algorithms, and confirm that the proposed AM‐ML‐MIFG algorithm has satisfactory performance in capturing the dynamic properties of the system.
Topics & Concepts
Multivariable calculusIdentification (biology)Class (philosophy)ForgettingMathematicsComputer scienceControl theory (sociology)Artificial intelligenceEngineeringControl engineeringPsychologyBiologyControl (management)Cognitive psychologyBotanyControl Systems and IdentificationGaussian Processes and Bayesian InferenceNeural Networks and Applications