Approximate gradients, convergence and positive realness in recursive identification of a class of non‐linear systems

DOI10.1002/acs.4480090403zbMath0834.93058OpenAlexW1968404693MaRDI QIDQ4854698

Publication date: 11 December 1995

Published in: International Journal of Adaptive Control and Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/acs.4480090403

zbMATH Keywords

convergence recursive identification adaptive filtering positive realness quantization Wiener model relay stochastic averaging output measurement saturation

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Adaptive control/observation systems (93C40) Identification in stochastic control theory (93E12)

Related Items (7)

On estimation of errors caused by non-linear undermodelling in system identification ⋮ Fixed-order FIR approximation of linear systems from quantized input and output data ⋮ MINLIP for the identification of monotone Wiener systems ⋮ On identification of FIR systems having quantized output data ⋮ Towards identification of Wiener systems with the least amount of a priori information on the nonlinearity ⋮ Robust distributed maximum likelihood estimation with dependent quantized data ⋮ Identification of Wiener systems with binary-valued output observations

Cites Work

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