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

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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 Cram’er-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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This work was supported by National Natural Science Foundation of China (Grant No. 61773054).

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Correspondence to Jin Guo.

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Guo, J., Diao, J. Prediction-based event-triggered identification of quantized input FIR systems with quantized output observations. Sci. China Inf. Sci. 63, 112201 (2020). https://doi.org/10.1007/s11432-018-9845-6

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Keywords identification

  • FIR systems
  • prediction-based event-triggered communication
  • quantized observations
  • convergence performance