73,545 research outputs found
Evolutionary Computation in High Energy Physics
Evolutionary Computation is a branch of computer science with which,
traditionally, High Energy Physics has fewer connections. Its methods were
investigated in this field, mainly for data analysis tasks. These methods and
studies are, however, less known in the high energy physics community and this
motivated us to prepare this lecture. The lecture presents a general overview
of the main types of algorithms based on Evolutionary Computation, as well as a
review of their applications in High Energy Physics.Comment: Lecture presented at 2006 Inverted CERN School of Computing; to be
published in the school proceedings (CERN Yellow Report
Hardware-in-the-loop tuning of a feedback controller for a buck converter using a GA
This paper presents a methodology for tuning a PID-based feedback controller for a buck converter using the ITAE controller performance index. The controller parameters are optimized to ensure that a reasonable transient response can be achieved whilst retaining stable operation. Experimental results demonstrate the versatility of the on-line tuning methodology
Recommended from our members
Artificial Immune Systems - Models, algorithms and applications
Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun
Results of Evolution Supervised by Genetic Algorithms
A series of results of evolution supervised by genetic algorithms with
interest to agricultural and horticultural fields are reviewed. New obtained
original results from the use of genetic algorithms on structure-activity
relationships are reported.Comment: 6 pages, 1 Table, 2 figure
Coherent control using adaptive learning algorithms
We have constructed an automated learning apparatus to control quantum
systems. By directing intense shaped ultrafast laser pulses into a variety of
samples and using a measurement of the system as a feedback signal, we are able
to reshape the laser pulses to direct the system into a desired state. The
feedback signal is the input to an adaptive learning algorithm. This algorithm
programs a computer-controlled, acousto-optic modulator pulse shaper. The
learning algorithm generates new shaped laser pulses based on the success of
previous pulses in achieving a predetermined goal.Comment: 19 pages (including 14 figures), REVTeX 3.1, updated conten
Robust control of room temperature and relative humidity using advanced nonlinear inverse dynamics and evolutionary optimisation
A robust controller is developed, using advanced nonlinear inverse dynamics (NID) controller design and genetic algorithm optimisation, for room temperature control. The performance is evaluated through application to a single zone dynamic building model. The proposed controller produces superior performance when compared to the NID controller optimised with a simple optimisation algorithm, and classical PID control commonly used in the buildings industry. An improved level of thermal comfort is achieved, due to fast and accurate tracking of the setpoints, and energy consumption is shown to be reduced, which in turn means carbon emissions are reduced
- âŠ