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This paper focuses on the identification of a class of multivariable systems with autoregressive noises. Two least squares-based algorithms are provided. One is the recursive generalized least squares algorithm. The idea is to integrate the colored noise regression terms into the information matrix and the noise parameters into the parameter vector,(More)
This paper studies the identification problems of input nonlinear controlled autoregressive moving average (IN-CARMA) systems, and derived an auxiliary model based recursive extended least squares (AM-RELS) algorithm and a maximum likelihood algorithm based on the Newton optimization method. The simulation results show that the proposed algorithm are(More)
This paper considers the parameter estimation problems of output error autoregressive (OEAR) systems based on the maximum likelihood principle and the gradient search principle. A maximum likelihood forgetting factor stochastic gradient algorithm and a maximum likelihood iterative gradient identification algorithm are developed. The simulation results show(More)
This paper investigates the model and polling mechanism in cluster monitoring system for seismic profession. First, we propose a PULL monitoring system model suitable for seismic observation equipment cluster. Second, we modify the general FCFS polling mechanism which limits the polling counts of important nodes in cluster and pose a new polling mechanism(More)
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