Identification of Wiener systems with quantized inputs and binary-valued output observations

DOI10.1016/j.automatica.2016.12.034zbMath1357.93101OpenAlexW2582902228MaRDI QIDQ518332

George Yin, Jin Guo, Yanlong Zhao, Ji-Feng Zhang, L. Y. Wang

Publication date: 28 March 2017

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2016.12.034

zbMATH Keywords

identificationasymptotic efficiencybinary-valued observationWiener systemquantized input

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Estimation and detection in stochastic control theory (93E10) Identification in stochastic control theory (93E12)

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Cites Work

• Unnamed Item
• Least squares based and gradient based iterative identification for Wiener nonlinear systems
• On identification of FIR systems having quantized output data
• Asymptotically efficient identification of FIR systems with quantized observations and general quantized inputs
• Block-oriented nonlinear system identification
• System identification with quantized observations
• The identification of nonlinear biological systems: Wiener and Hammerstein Cascade models
• Input design in worst-case system identification with quantized measurements
• Recursive projection algorithm on FIR system identification with binary-valued observations
• Maximum likelihood identification of Wiener models
• Identification of Wiener systems with binary-valued output observations
• Identification of Hammerstein Systems with Quantized Observations
• Fading memory and the problem of approximating nonlinear operators with Volterra series
• System identification using binary sensors
• Recursive Identification and Parameter Estimation