Particle filtering-based recursive identification for controlled auto-regressive systems with quantised output

DOI10.1049/IET-CTA.2019.0028zbMATH Open1544.93808MaRDI QIDQ6598667

Jie Ding, Jinxing Lin, Guo-Ping Jiang, Jiazhong Chen

Publication date: 5 September 2024

Published in: IET Control Theory \& Applications (Search for Journal in Brave)

zbMATH Keywords

parameter estimationstochastic processesprobabilitygradient methodsautoregressive processesrecursive estimationfiltering theoryleast squares approximationsrecursive prediction error methodposterior probability density functionauxiliary model principleauxiliary model-basedcontrolled auto-regressive systemsdiscrete random sampling pointsinvalid particleslinear output estimatesmain toolsnovel particle filtering techniquenovel recursive identification algorithmparticle filtering (numerical methods)particle filtering technique-based algorithmparticle filtering-based recursive identificationquantised outputstandard stochastic gradient algorithm

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Least squares and related methods for stochastic control systems (93E24) Identification in stochastic control theory (93E12)

Cites Work

• Convergence analysis for recursive Hammerstein identification
• Identification of Wiener systems with quantized inputs and binary-valued output observations
• Parameter estimation with scarce measurements
• On identification of FIR systems having quantized output data
• Gradient iterative algorithm for dual-rate nonlinear systems based on a novel particle filter
• Asymptotically efficient identification of FIR systems with quantized observations and general quantized inputs
• Recusrsive prediction error identification using the nonlinear Wiener model
• A hierarchical least squares identification algorithm for Hammerstein nonlinear systems using the key term separation
• Modified multi-innovation stochastic gradient algorithm for Wiener-Hammerstein systems with backlash
• Recursive parameter estimation algorithm for multivariate output-error systems
• Auxiliary model method for transfer function estimation from noisy input and output data
• Recursive maximum likelihood method for the identification of Hammerstein ARMAX system
• Model recovery for Hammerstein systems using the auxiliary model based orthogonal matching pursuit method
• Epidemics on small worlds of tree-based wireless sensor networks
• Identification of Wiener systems with binary-valued output observations
• Hybrid Control of a Bioreactor With Quantized Measurements
• Approximate gradients, convergence and positive realness in recursive identification of a class of non‐linear systems
• A Weighted Least-Squares Approach to Parameter Estimation Problems Based on Binary Measurements
• System identification using binary sensors
• Energy-based balance control approach to the ball and beam system

Related Items (6)

Recursive search-based identification algorithms for the exponential autoregressive time series model with coloured noise ⋮ Hierarchical multi-innovation generalised extended stochastic gradient methods for multivariable equation-error autoregressive moving average systems ⋮ Recursive parameter estimation and its convergence for bilinear systems ⋮ Improved least-squares identification for multiple-output non-linear stochastic systems ⋮ Partially-coupled gradient-based iterative algorithms for multivariable output-error-like systems with autoregressive moving average noises ⋮ Parameter estimation for a multi-input multi-output state-space system with unmeasurable states through the data filtering technique

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