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

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

Torbjörn Wigren

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

convergencerecursive identificationadaptive filteringpositive realnessquantizationWiener modelrelaystochastic averagingoutput 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

• Avoiding ill-convergence of finite dimensional blind adaptation schemes excited by discrete symbol sequences
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Approximate gradients, convergence and positive realness in recursive identification of a class of non‐linear systems