5,468 research outputs found
Convergence of adaptive mixtures of importance sampling schemes
In the design of efficient simulation algorithms, one is often beset with a
poor choice of proposal distributions. Although the performance of a given
simulation kernel can clarify a posteriori how adequate this kernel is for the
problem at hand, a permanent on-line modification of kernels causes concerns
about the validity of the resulting algorithm. While the issue is most often
intractable for MCMC algorithms, the equivalent version for importance sampling
algorithms can be validated quite precisely. We derive sufficient convergence
conditions for adaptive mixtures of population Monte Carlo algorithms and show
that Rao--Blackwellized versions asymptotically achieve an optimum in terms of
a Kullback divergence criterion, while more rudimentary versions do not benefit
from repeated updating.Comment: Published at http://dx.doi.org/10.1214/009053606000001154 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
An optimized tuned mass damper/harvester device
Much work has been conducted on vibration absorbers, such as tuned mass dampers (TMD), where significant energy is extracted from a structure. Traditionally, this energy is dissipated through the devices as heat. In this paper, the concept of recovering some of this energy electrically and reuse it for structural control or health monitoring is investigated. The energy-dissipating damper of a TMD is replaced with an electromagnetic device in order to transform mechanical vibration into electrical energy. That gives the possibility of controlled damping force whilst generating useful electrical energy. Both analytical and experimental results from an adaptive and a semi-active tuned mass damper/harvester are presented. The obtained results suggest that sufficient energy might be harvested for the device to tune itself to optimise vibration suppression
Optimization and sensitivity analysis of computer simulation models by the score function method
Experimental Design;Simulation;Optimization;Queueing Theory
Identification of Nonlinear Systems From the Knowledge Around Different Operating Conditions: A Feed-Forward Multi-Layer ANN Based Approach
The paper investigates nonlinear system identification using system output
data at various linearized operating points. A feed-forward multi-layer
Artificial Neural Network (ANN) based approach is used for this purpose and
tested for two target applications i.e. nuclear reactor power level monitoring
and an AC servo position control system. Various configurations of ANN using
different activation functions, number of hidden layers and neurons in each
layer are trained and tested to find out the best configuration. The training
is carried out multiple times to check for consistency and the mean and
standard deviation of the root mean square errors (RMSE) are reported for each
configuration.Comment: "6 pages, 9 figures; The Second IEEE International Conference on
Parallel, Distributed and Grid Computing (PDGC-2012), December 2012, Solan
Adaptive Multiple Importance Sampling for Gaussian Processes
In applications of Gaussian processes where quantification of uncertainty is
a strict requirement, it is necessary to accurately characterize the posterior
distribution over Gaussian process covariance parameters. Normally, this is
done by means of standard Markov chain Monte Carlo (MCMC) algorithms. Motivated
by the issues related to the complexity of calculating the marginal likelihood
that can make MCMC algorithms inefficient, this paper develops an alternative
inference framework based on Adaptive Multiple Importance Sampling (AMIS). This
paper studies the application of AMIS in the case of a Gaussian likelihood, and
proposes the Pseudo-Marginal AMIS for non-Gaussian likelihoods, where the
marginal likelihood is unbiasedly estimated. The results suggest that the
proposed framework outperforms MCMC-based inference of covariance parameters in
a wide range of scenarios and remains competitive for moderately large
dimensional parameter spaces.Comment: 27 page
Recommended from our members
Chapter 2Â -Â Data-Driven Energy Efficient Driving Control in Connected Vehicle Environment
Dynamic importance sampling for uniformly recurrent markov chains
Importance sampling is a variance reduction technique for efficient
estimation of rare-event probabilities by Monte Carlo. In standard importance
sampling schemes, the system is simulated using an a priori fixed change of
measure suggested by a large deviation lower bound analysis. Recent work,
however, has suggested that such schemes do not work well in many situations.
In this paper we consider dynamic importance sampling in the setting of
uniformly recurrent Markov chains. By ``dynamic'' we mean that in the course of
a single simulation, the change of measure can depend on the outcome of the
simulation up till that time. Based on a control-theoretic approach to large
deviations, the existence of asymptotically optimal dynamic schemes is
demonstrated in great generality. The implementation of the dynamic schemes is
carried out with the help of a limiting Bellman equation. Numerical examples
are presented to contrast the dynamic and standard schemes.Comment: Published at http://dx.doi.org/10.1214/105051604000001016 in the
Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute
of Mathematical Statistics (http://www.imstat.org
- …