165 research outputs found
Reduction method with simulated annealing for semi-infinite programming
Semi-infinite programming (SIP) problems are characterized by a finite number of variables and an infinite number of constraints. The class of the reduction methods is based on the idea that, under certain conditions, it is possible to replace the infinite constraints by a finite set of constraints, that are locally sufficient to define the feasible region of the SIP problem.
We propose a new reduction method based on a simulated annealing algorithm for multi-local optimization and the penalty method for solving the finite problem
Caracterização da função de penalidade exponencial na resolução de problemas de programação semi-infinita
Os problemas de programação semi-infinita (PSI) são caracterizados por terem um conjunto finito de variáveis e um número infinito de restrições. A classe de métodos de redução é baseada na ideia que, sob certas condições, é possível substituir as infinitas restrições por um conjunto finito de restrições que, localmente, é suficiente para definir a região admissível do problema PSI.
Neste trabalho é proposto um novo método de redução que combina o método simulated annealing para a optimização multilocal, e o método de penalidade para a optimização não linear com restrições
Using Artificial Intelligence for Model Selection
We apply the optimization algorithm Adaptive Simulated Annealing (ASA) to the
problem of analyzing data on a large population and selecting the best model to
predict that an individual with various traits will have a particular disease.
We compare ASA with traditional forward and backward regression on computer
simulated data. We find that the traditional methods of modeling are better for
smaller data sets whereas a numerically stable ASA seems to perform better on
larger and more complicated data sets.Comment: 10 pages, no figures, in Proceedings, Hawaii International Conference
on Statistics and Related Fields, June 5-8, 200
Nonlinear continuous global optimization by modified differential evolution
The task of global optimization is to find a point where the objective function obtains its most extreme value.
Differential evolution (DE) is a population-based heuristic approach that creates new candidate solutions by combining several
points of the same population. The algorithm has three parameters: amplification factor of the differential variation, crossover
control parameter and population size. It is reported that DE is sensitive to the choice of these parameters. To improve
the quality of the solution, in this paper, we propose a modified differential evolution introducing self-adaptive parameters,
modified mutation and the inversion operator. We test our method with a set of nonlinear continuous optimization problems
with simple bounds.Fundação para a Ciência e a Tecnologia (FCT
A Unifying Framework for Finite Wordlength Realizations.
A general framework for the analysis of the finite
wordlength (FWL) effects of linear time-invariant digital filter
implementations is proposed. By means of a special implicit system
description, all realization forms can be described. An algebraic
characterization of the equivalent classes is provided, which
enables a search for realizations that minimize the FWL effects
to be made. Two suitable FWL coefficient sensitivity measures
are proposed for use within the framework, these being a transfer
function sensitivity measure and a pole sensitivity measure. An
illustrative example is presented
Limiting the effects of earthquakes on gravitational-wave interferometers
Ground-based gravitational wave interferometers such as the Laser
Interferometer Gravitational-wave Observatory (LIGO) are susceptible to
high-magnitude teleseismic events, which can interrupt their operation in
science mode and significantly reduce the duty cycle. It can take several hours
for a detector to stabilize enough to return to its nominal state for
scientific observations. The down time can be reduced if advance warning of
impending shaking is received and the impact is suppressed in the isolation
system with the goal of maintaining stable operation even at the expense of
increased instrumental noise. Here we describe an early warning system for
modern gravitational-wave observatories. The system relies on near real-time
earthquake alerts provided by the U.S. Geological Survey (USGS) and the
National Oceanic and Atmospheric Administration (NOAA). Hypocenter and
magnitude information is generally available in 5 to 20 minutes of a
significant earthquake depending on its magnitude and location. The alerts are
used to estimate arrival times and ground velocities at the gravitational-wave
detectors. In general, 90\% of the predictions for ground-motion amplitude are
within a factor of 5 of measured values. The error in both arrival time and
ground-motion prediction introduced by using preliminary, rather than final,
hypocenter and magnitude information is minimal. By using a machine learning
algorithm, we develop a prediction model that calculates the probability that a
given earthquake will prevent a detector from taking data. Our initial results
indicate that by using detector control configuration changes, we could prevent
interruption of operation from 40-100 earthquake events in a 6-month
time-period
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