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This paper presents two recursive identification algorithms for bilinear-parameter models: a decomposition based stochastic gradient algorithm and a decomposition based recursive least squares algorithm. The key is to decompose a bilinear-parameter model into two fictitious subsystems, and to identify the parameters of each subsystem by replacing the unknown variables in the information vectors with...
Based on the filtering theory, we present a filtering based recursive least squares algorithm for a class of Wiener nonlinear systems. The basic idea is to use an estimated noise transfer function to filter the input-ouput data, to obtain two identification models containing the parameters of the system model and the noise model, respectively, and to present the filtering based recursive least squares...
This paper proposes a design method of the proportional integral derivative controller. The controller is used to a delay-time system. A polynomial approximation method is studied to the delay-time system. The controller is designed based on the approximation model and used to the delay-time system. Some numerical examples are illustrated to show the effective of the proposed method.
This paper studies the convergence of the auxiliary model based stochastic gradient parameter estimation algorithm for multi-input output-error systems by using the martingale convergence theorem. The basic idea is to formulate a positive definite function of the parameter estimation error and to indicate that the parameter estimates converge to their true values under persistent excitation. The proposed...
This paper studies identification problems for two-input multirate systems with colored noises (The method in the paper can be easily extended to multi-input multirate systems). The state-space models are derived for the multirate systems with two different input sampling periods and furthermore the corresponding transfer functions are obtained. To solve the difficulty of identification models with...
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