Asymptotically efficient non-truncated identification for FIR systems with binary-valued outputs

Wang, Ting; Tan, Jianwei; Zhao, Yanlong

doi:10.1007/s11432-018-9646-7

Cited by 12 publications

(8 citation statements)

References 9 publications

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“…The parameter identification of Wiener system was also studied in terms of quantized inputs and binary outputs in [26]. Based on the truncated empirical measurement method of probabilistic statistics, a non-truncated empirical measurement method was proposed for the finite impulse response (FIR) system model with binary outputs, and the progressive effectiveness of the algorithm was demonstrated in [27]. A two-segment design method for a class of FIR systems with binaryvalued observations was investigated in [28].…”

Section: Introductionmentioning

confidence: 99%

Pattern-Moving-Based Parameter Identification of Output Error Models with Multi-Threshold Quantized Observations

Li¹,

Xu²,

Han³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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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.

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Section: Introductionmentioning

confidence: 99%

Pattern-Moving-Based Parameter Identification of Output Error Models with Multi-Threshold Quantized Observations

Li¹,

Xu²,

Han³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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“…The quantization is a process from a large set (such as the set of all real numbers) to a smaller set (often a finite set of discrete values); hence, it can substantially simplify the observation value domain Z. This can be directly grasped from the case of binary-valued quantized observation in which Z = {0, 1} [15][16][17]. With the rapid advancement in micro-sensors, communication technologies and many frontier fields, the system identification with quantized observations has received considerable research attention [16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [17] discussed the asymptotically efficient non-truncated identification on finite impulse response (FIR) systems with binary-valued outputs. Under quantized inputs and quantized output observations, the identification of FIR systems was investigated in [18], where estimation algorithms and their convergence performance were established.…”

Section: Introductionmentioning

confidence: 99%

Prediction-based event-triggered identification of quantized input FIR systems with quantized output observations

Guo

Diao

2019

Sci. China Inf. Sci.

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This paper addresses the identification of finite impulse response (FIR) systems with both quantized and event-triggered observations. An event-triggered communication scheme for the binary-valued output quantization is introduced to save communication resources. Combining the empirical-measure-based identification technique and the weighted least-squares optimization, an algorithm is proposed to estimate the unknown parameter by full use of the received data and the not-triggered condition. Under quantized inputs, it is shown that the estimate can strongly converge to the real values and the estimator is asymptotically efficient in terms of the Cramér-Rao lower bound. Further, the limit of the average communication rate is derived and the tradeoff between this limit and the estimation performance is discussed. Moreover, the case of multi-threshold quantized observations is considered. Numerical examples are included to illustrate the obtained main results.

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“…Besides, they can also be categorized in terms of the quantization schemes, e.g., fixed-level quantizer and uniform quantizer. In particular, for FIR models with finite quantization levels, there are empirical measure approach [9,17] which has good properties of unbiasedness and strong consistency, EM algorithm [10,11] which has a exponent convergent speed under certain conditions, kernel-based method [12], and quadratic programming-based method [13].…”

Section: Introductionmentioning

confidence: 99%

FIR system identification with set-valued and precise observations from multiple sensors

Zhang

Wang

Zhao

2019

Sci. China Inf. Sci.

Self Cite

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This paper considers the system identification problem for FIR (finite impulse response) systems with set-valued and precise observations received from multiple sensors. A fusion estimation algorithm based on some suitable identification algorithms for different types of observations is proposed. In particular, least square method is chosen for FIR systems with precise observations, while empirical measure method and EM algorithm are chosen for FIR systems with set-valued observations in the cases of periodic and general system inputs, respectively. Then, the quasi-convex combination estimator (QCCE) fusing the two different estimators by a linear combination with appropriate weights is constructed. Furthermore, the convergence properties are theoretically analyzed in terms of strong consistency and asymptotic efficiency. The fused estimator QCCE is proved to achieve the Cramér-Rao (CR) lower bound asymptotically under periodic inputs. Extensive numerical simulations validate the superiority of the fusion estimation algorithm under both periodic and general inputs.

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Asymptotically efficient non-truncated identification for FIR systems with binary-valued outputs

Cited by 12 publications

References 9 publications

Pattern-Moving-Based Parameter Identification of Output Error Models with Multi-Threshold Quantized Observations

Pattern-Moving-Based Parameter Identification of Output Error Models with Multi-Threshold Quantized Observations

Prediction-based event-triggered identification of quantized input FIR systems with quantized output observations

FIR system identification with set-valued and precise observations from multiple sensors

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