Abstract:

System identification based on gradient optimization search is proposed for parameter estimation of stochastic state-space systems.The system parameters are estimated by optimizing an output-error cost function.Moreover，the search direction is determined by dynamic singular ration of Jacobian matrix for the purpose of solving the algorithm failure caused by the rank-deficient Jacobians.In addition，identification algorithm by considering the local linear approximation of the output error is presented.Furthermore，the analytic expression of the convergence rate of the identification algorithm is also given.Finally，the effectiveness of the proposed method is illustrated by numerical simulation.