Stochastic average gradient algorithm for multirate FIR models with varying time delays using self‐organizing maps

DOI10.1002/acs.3116zbMath1469.93108OpenAlexW3020457926MaRDI QIDQ5003430

Qianyan Shen, Jing Chen, Yanjun Liu, Junxia Ma

Publication date: 22 July 2021

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.3116

zbMATH Keywords

parameter estimationself-organizing mapvarying time delayFIR modelstochastic average gradient algorithm

Mathematics Subject Classification ID

Identification in stochastic control theory (93E12) Delay control/observation systems (93C43) Impulsive control/observation systems (93C27)

Related Items (12)

Model recovery for multi-input signal-output nonlinear systems based on the compressed sensing recovery theory ⋮ Three‐stage least squares‐based iterative estimation algorithms for bilinear state‐space systems based on the bilinear state estimator ⋮ Hierarchical maximum likelihood generalized extended stochastic gradient algorithms for bilinear‐in‐parameter systems ⋮ Filtering‐based multi‐innovation recursive identification methods for input nonlinear systems with piecewise‐linear nonlinearity based on the optimization criterion ⋮ Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems ⋮ Modified particle filtering‐based robust estimation for a networked control system corrupted by impulsive noise ⋮ Iterative parameter identification algorithms for the generalized time‐varying system with a measurable disturbance vector ⋮ Augmented flexible least squares algorithm for time‐varying parameter systems ⋮ A novel dynamic nonlinear partial least squares based on the cascade structure ⋮ Kernel‐based regularization least squares algorithm for nonlinear time‐delayed systems using self‐organizing maps ⋮ Partially-coupled nonlinear parameter optimization algorithm for a class of multivariate hybrid models ⋮ Parameter estimation for nonlinear Volterra systems by using the multi-innovation identification theory and tensor decomposition

Cites Work

• Unnamed Item
• Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling
• Minimizing finite sums with the stochastic average gradient
• Parameter estimation for nonlinear time-delay systems with noisy output measurements
• Event-triggering dissipative control of switched stochastic systems via sliding mode
• Model recovery for Hammerstein systems using the hierarchical orthogonal matching pursuit method
• Variational Bayesian approach for ARX systems with missing observations and varying time-delays
• Adaptive sliding mode fault-tolerant control for type-2 fuzzy systems with distributed delays
• Gradient estimation algorithms for the parameter identification of bilinear systems using the auxiliary model
• Robust parameter estimation for nonlinear multistage time-delay systems with noisy measurement data
• Model recovery for Hammerstein systems using the auxiliary model based orthogonal matching pursuit method
• Particle filtering based parameter estimation for systems with output-error type model structures
• A new filter‐based stochastic gradient algorithm for dual‐rate ARX models
• Combined state and parameter estimation for Hammerstein systems with time delay using the Kalman filtering
• Adaptive filtering‐based multi‐innovation gradient algorithm for input nonlinear systems with autoregressive noise
• A Regularized Variable Projection Algorithm for Separable Nonlinear Least Squares Problems
• Partially Coupled Stochastic Gradient Identification Methods for Non-Uniformly Sampled Systems
• The innovation algorithms for multivariable state‐space models
• Adaptive output-feedback synchronisation of electromechanical systems under time-varying communication delays
• State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors
• The filtering‐based maximum likelihood iterative estimation algorithms for a special class of nonlinear systems with autoregressive moving average noise using the hierarchical identification principle
• A Convergent Incremental Gradient Method with a Constant Step Size

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