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Editors: Sequential Monte Carlo Methods in Practice
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The article was published on 2001-01-01 and is currently open access. It has received 1215 citations till now. The article focuses on the topics: Dynamic Monte Carlo method & Monte Carlo method in statistical physics.read more
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Journal ArticleDOI
State Feedback Control using Particle Filter
TL;DR: A construction method for a state feedback control system using a particle filter as an observer for probabilistic state estimation is described, and a maximum a-posteriori probability estimation extraction method and an method for evaluation of the effective sample size have been incorporated into the particle filter.
Proceedings Article
Advances in cost-reference particle filtering
TL;DR: This paper investigates several variants of CRPF, a particle filtering-type methodology, which is not based on any particular probabilistic assumptions regarding the studied dynamic model, and compares them with standard particle filtering (SPF).
Journal ArticleDOI
Nonlinear State Estimation and Control for Freeway On-Ramp Metering
TL;DR: In this article, a modified second-order continuum macroscopic model is proposed for freeway ramp metering using a differential flatness concept, which is deployed in the cases when the traffic data provided by loop detectors or any measurements device, are partially unknown or missed.
Proceedings ArticleDOI
Artificial neural networks for stochastic control of nonliner state space systems
TL;DR: A multi layer perceptron (MLP) neural network is considered to represent the general structure of the controller and an expectation maximization (EM) algorithm joint with the particle smoothing framework are proposed for updating parameters of the MLP network.
Journal ArticleDOI
Sequential Monte Carlo methods for stochastic volatility models: a review
TL;DR: The SequentialMonte Carlo methods, also known as particle filters, for estimation and pricing in stochastic volatility models with general noises are reviewed.