Identification of sparse FIR systems using a general quantisation scheme

DOI10.1080/00207179.2013.861611zbMath1291.93310OpenAlexW2063146103MaRDI QIDQ5494503

Juan I. Yuz, Graham C. Goodwin, Boris I. Godoy, Rodrigo Carvajal, Juan C. Agüero

Publication date: 28 July 2014

Published in: International Journal of Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207179.2013.861611

zbMATH Keywords

system identificationsparsitymaximum likelihood (ML)quantized systems

Mathematics Subject Classification ID

System identification (93B30) Identification in stochastic control theory (93E12)

Related Items

Variational Bayesian inference for the identification of FIR systems via quantized output data ⋮ A new kernel-based approach to system identification with quantized output data ⋮ Event-triggered identification of FIR systems with binary-valued output observations ⋮ Continuous-time model identification: application on a behavioural (miLife) study

Cites Work

• \(\ell_{1}\)-penalization for mixture regression models
• On identification of FIR systems having quantized output data
• One-step sparse estimates in nonconcave penalized likelihood models
• Statistical results for system identification based on quantized observations
• System identification with quantized observations
• Mixtures of distributions, moment inequalities and measures of exponentiality and normality
• Identification of ARMA models using intermittent and quantized output observations
• Inference in hidden Markov models.
• Identification of Wiener systems with binary-valued output observations
• Sequential Monte Carlo Methods in Practice
• Learning Multiscale Sparse Representations for Image and Video Restoration
• On scale mixtures of normal distributions
• Normal Variance-Mean Mixtures and z Distributions
• Channel identification for high speed digital communications
• Monotone Operators and the Proximal Point Algorithm
• Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM Algorithm
• Linear Regression With a Sparse Parameter Vector
• System identification using binary sensors
• Regularization and Variable Selection Via the Elastic Net
• Data augmentation for non-Gaussian regression models using variance-mean mixtures
• Model Selection and Estimation in Regression with Grouped Variables
• For most large underdetermined systems of linear equations the minimal 𝓁₁‐norm solution is also the sparsest solution
• Compressed sensing