Auxiliary model‐based recursive least squares and stochastic gradient algorithms and convergence analysis for feedback nonlinear output‐error systems
Guangqin Miao, Dan Yang, Feng Ding
Abstract
Summary This paper deals with the problem of the parameter estimation for feedback nonlinear output‐error systems. The auxiliary model‐based recursive least squares algorithm and the auxiliary model‐based stochastic gradient algorithm are derived for parameter estimation. Based on the stochastic process theory, the convergence of the proposed algorithms are proved. The simulation results indicate that the proposed algorithms can estimate the parameters of feedback nonlinear output‐error systems effectively.
Topics & Concepts
Convergence (economics)Nonlinear systemRecursive least squares filterStochastic approximationAlgorithmEstimation theoryControl theory (sociology)Process (computing)Least-squares function approximationComputer scienceStochastic processNon-linear least squaresMathematicsMathematical optimizationAdaptive filterArtificial intelligenceKey (lock)StatisticsOperating systemEconomic growthComputer securityQuantum mechanicsEstimatorPhysicsEconomicsControl (management)Control Systems and IdentificationFault Detection and Control SystemsNeural Networks and Applications