Estimation of parameters in the state space model of stochastic RL electrical circuit
DOI10.1108/03321641311306141zbMath1282.78040OpenAlexW2028511333MaRDI QIDQ2842752
Publication date: 16 August 2013
Published in: COMPEL - The international journal for computation and mathematics in electrical and electronic engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1108/03321641311306141
parameter estimationcircuitsoptimal filteringstate space modelelectrical engineeringRL electrical circuit
Monte Carlo methods (65C05) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Stochastic programming (90C15) Composite media; random media in optics and electromagnetic theory (78A48) Optimization problems in optics and electromagnetic theory (78M50) Monte Carlo methods applied to problems in optics and electromagnetic theory (78M31)
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Cites Work
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- Particle approximations of the score and observed information matrix in state space models with application to parameter estimation
- Parameter estimation for time varying nonlinear circuit from state analysis and simulation
- Sequential Monte Carlo smoothing with application to parameter estimation in nonlinear state space models
- Maximum likelihood identification of Wiener models
- Following a Moving Target—Monte Carlo Inference for Dynamic Bayesian Models
- A stochastic perspective of RL electrical circuit using different noise terms
- State space realization of bilinear continuous-time input–output equations
- Reversible Jump, Birth-and-Death and More General Continuous Time Markov Chain Monte Carlo Samplers
- On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems
- Nonlinear Smoothing Theory
- Dynamic learning with the EM algorithm for neural networks
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