This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-thresho...This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-threshold quantized observations.It proves the convergence of the designed algorithm.A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output(SIMO)or SISO nonlinear systems,and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system.The system input design is accomplished using the measurement technology of random repeatability test,and the probabilistic characteristic of the explicit metric value is employed to estimate the implicit metric value of the pattern class variable.A modified auxiliary model stochastic gradient recursive algorithm(M-AM-SGRA)is designed to identify the model parameters,and the contraction mapping principle proves its convergence.Two numerical examples are given to demonstrate the feasibility and effectiveness of the achieved identification algorithm.展开更多
In networked system identification,how to effectively use communication resources and improve convergence speed is the focus of attention.However,there is an inherent contradiction between the two tasks.In this paper,...In networked system identification,how to effectively use communication resources and improve convergence speed is the focus of attention.However,there is an inherent contradiction between the two tasks.In this paper,the event-driven communication is used to save communication resources for the identification of finite impulse response systems,and the input design is carried out to meet the requirements of convergence speed.First,a difference-driven communication is proposed.Then,the performance of the communication mechanism is analyzed,and the calculation method of its communication rate is given.After that,according to the communication rate and the convergence rate of the identification algorithm,the input design problem is transformed into a constrained optimization problem,and the algorithm for finding the optimal solution is given.In addition,considering the case that the output is quantized by multiple thresholds,the way to calculate its communication rate is given and the influence of threshold number on communication rate is discussed.Finally,the effectiveness of the algorithm is verified by simulation.展开更多
This paper investigates the FIR systems identification with quantized output observations and a large class of quantized inputs. The limit inferior of the regressors' frequencies of occurrences is employed to char...This paper investigates the FIR systems identification with quantized output observations and a large class of quantized inputs. The limit inferior of the regressors' frequencies of occurrences is employed to characterize the input's persistent excitation, under which the strong convergence and the convergence rate of the two-step estimation algorithm are given. As for the asymptotical efficiency,with a suitable selection of the weighting matrix in the algorithm, even though the limit of the product of the Cram′er-Rao(CR) lower bound and the data length does not exist as the data length goes to infinity, the estimates still can be asymptotically efficient in the sense of CR lower bound. A numerical example is given to demonstrate the effectiveness and the asymptotic efficiency of the algorithm.展开更多
基金This work was supported by the National Natural Science Foundation of China(62076025).
文摘This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-threshold quantized observations.It proves the convergence of the designed algorithm.A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output(SIMO)or SISO nonlinear systems,and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system.The system input design is accomplished using the measurement technology of random repeatability test,and the probabilistic characteristic of the explicit metric value is employed to estimate the implicit metric value of the pattern class variable.A modified auxiliary model stochastic gradient recursive algorithm(M-AM-SGRA)is designed to identify the model parameters,and the contraction mapping principle proves its convergence.Two numerical examples are given to demonstrate the feasibility and effectiveness of the achieved identification algorithm.
基金supported in part by the National Natural Science Foundation of China(No.62173030)in part by the Beijing Natural Science Foundation(No.4222050).
文摘In networked system identification,how to effectively use communication resources and improve convergence speed is the focus of attention.However,there is an inherent contradiction between the two tasks.In this paper,the event-driven communication is used to save communication resources for the identification of finite impulse response systems,and the input design is carried out to meet the requirements of convergence speed.First,a difference-driven communication is proposed.Then,the performance of the communication mechanism is analyzed,and the calculation method of its communication rate is given.After that,according to the communication rate and the convergence rate of the identification algorithm,the input design problem is transformed into a constrained optimization problem,and the algorithm for finding the optimal solution is given.In addition,considering the case that the output is quantized by multiple thresholds,the way to calculate its communication rate is given and the influence of threshold number on communication rate is discussed.Finally,the effectiveness of the algorithm is verified by simulation.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61174042 and 61403027the National Key Research and Development Program of China(2016YFB0901902)the Open Research Project under Grant No.20160105 from SKLMCCS
文摘This paper investigates the FIR systems identification with quantized output observations and a large class of quantized inputs. The limit inferior of the regressors' frequencies of occurrences is employed to characterize the input's persistent excitation, under which the strong convergence and the convergence rate of the two-step estimation algorithm are given. As for the asymptotical efficiency,with a suitable selection of the weighting matrix in the algorithm, even though the limit of the product of the Cram′er-Rao(CR) lower bound and the data length does not exist as the data length goes to infinity, the estimates still can be asymptotically efficient in the sense of CR lower bound. A numerical example is given to demonstrate the effectiveness and the asymptotic efficiency of the algorithm.
