2 research outputs found
Two Identification Methods for Dual-Rate Sampled-Data Nonlinear Output-Error Systems
This paper presents two methods for dual-rate sampled-data nonlinear output-error systems. One
method is the missing output estimation based stochastic gradient identification algorithm and the other
method is the auxiliary model based stochastic gradient identification algorithm. Different from the
polynomial transformation based identification methods, the two methods in this paper can estimate
the unknown parameters directly. A numerical example is provided to confirm the effectiveness of the
proposed methods
Parameter and State Estimator for State Space Models
This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective