:: Volume 12, Issue 2 (3-2016) ::
JSRI 2016, 12(2): 129-146 Back to browse issues page
Skew Normal State Space Modeling of RC Electrical Circuit and Parameters Estimation based on Particle Markov Chain Monte Carlo
R. Farnoosh *1, A. Hajrajabi
1- , rfarnoosh@iust.ac.ir
Abstract:   (3580 Views)

Received: 9/21/2013      Approved: 12/9/2015‎

Abstract: In this paper, a skew normal state space model of RC electrical circuit is presented by considering the stochastic differential equation of the this circuit as the dynamic model with colored and white noise and considering a skew normal distribution instead of normal as the measurement noise distribution. Optimal filtering technique via sequential Monte Carlo perspective is developed for tracking the charge as the hidden state of this model. Furthermore, it is assumed that this model contains unknown parameters (resistance, capacitor, mean, variance and shape parameter of the skew normal as the measurement noise distribution). Bayesian framework is applied for estimation of both the hidden charge and the unknown parameters using particle marginal Metropolis-Hastings scheme. It is shown that the coverage percentage of skew normal is more than the one of normal as the measurement noise. Some simulation studies are carried out to demonstrate the efficiency of the proposed approaches.

Keywords: RC electrical circuit, state space model, sequential Monte Carlo filtering, parameter estimation.
Full-Text [PDF 383 kb]   (986 Downloads)    
Type of Study: Research | Subject: General
Received: 2016/07/4 | Accepted: 2016/07/4 | Published: 2016/07/4

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Volume 12, Issue 2 (3-2016) Back to browse issues page