214,437 research outputs found
Extracting quantum dynamics from genetic learning algorithms through principal control analysis
Genetic learning algorithms are widely used to control ultrafast optical
pulse shapes for photo-induced quantum control of atoms and molecules. An
unresolved issue is how to use the solutions found by these algorithms to learn
about the system's quantum dynamics. We propose a simple method based on
covariance analysis of the control space, which can reveal the degrees of
freedom in the effective control Hamiltonian. We have applied this technique to
stimulated Raman scattering in liquid methanol. A simple model of two-mode
stimulated Raman scattering is consistent with the results.Comment: 4 pages, 5 figures. Presented at coherent control Ringberg conference
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Comparative performance of intelligent algorithms for system identification and control
This paper presents an investigation into the comparative performance of intelligent system identification and control algorithms within the framework of an active vibration control (AVC) system. Evolutionary Genetic algorithms (GAs) and Adaptive Neuro-Fuzzy Inference system (ANFIS) algorithms are used to develop mechanisms of an AVC system, where the controller is designed based on optimal vibration suppression using the plant model. A simulation platform of a flexible beam system in transverse vibration using finite difference (FD) method is considered to demonstrate the capabilities of the AVC system using GAs and ANFIS. MATLAB GA tool box for GAs and Fuzzy Logic tool box for ANFIS function are used to design the AVC system. The system is men implemented, tested and its performance assessed for GAs and ANFIS based algorithms. Finally, a comparative performance of the algorithms in implementing system identification and corresponding AVC system using GAs and ANFIS is presented and discussed through a set of experiments
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Intelligent Learning Algorithms for Active Vibration Control
YesThis correspondence presents an investigation into the
comparative performance of an active vibration control (AVC) system
using a number of intelligent learning algorithms. Recursive least square
(RLS), evolutionary genetic algorithms (GAs), general regression neural
network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS)
algorithms are proposed to develop the mechanisms of an AVC system.
The controller is designed on the basis of optimal vibration suppression
using a plant model. A simulation platform of a flexible beam system
in transverse vibration using a finite difference method is considered to
demonstrate the capabilities of the AVC system using RLS, GAs, GRNN,
and ANFIS. The simulation model of the AVC system is implemented,
tested, and its performance is assessed for the system identification models
using the proposed algorithms. Finally, a comparative performance of the
algorithms in implementing the model of the AVC system is presented and
discussed through a set of experiments
Optimization of a small passive wind turbine generator with multiobjective genetic algorithms
In this paper Multiobjective Genetic Algorithms (MOGAs) are used for the design of a small wind turbine generator (WTG) coupled to a DC bus through a diode bridge. The originality of the considered system resides in the suppression of the Maximum Power Point Tracker (MPPT). The poor efficiency of the corresponding passive structure is considerably improved by optimizing the generator characteristics associated with the wind turbine in relation to the wind cycle. The optimized configurations are capable of matching very closely the behavior of active wind turbine systems which operate at optimal wind powers by using a MPPT control device
Manufacturing process planning optimisation in reconfigurable multiple parts flow lines
Purpose: This paper explores the capabilities of genetic algorithms in handling optimization of the critical issues mentioned above for the purpose of manufacturing process planning in reconfigurable manufacturing activities. Two modified genetic algorithms are devised and employed to provide the best approximate process planning solution. Modifications included adapting genetic operators to the problem specific knowledge and implementing application specific heuristics to enhance the search efficiency.
Design/methodology/approach: The genetic algorithm methodology implements a genetic algorithm that is augmented by application specific heuristics in order to guide the search for an optimal solution. The case study is based on the manufacturing system. Raw materials enter the system through an input stage and exit the system through an output stage. The system is composed of sixteen (16) processing modules that are arranged in four processing stages.
Findings: The results indicate that the two genetic algorithms are able to converge to optimal solutions in reasonable time. A computational study shows that improved solutions can be obtained by implementing a genetic algorithm with an extended diversity control mechanism.
Research limitations/implications: This paper has examined the issues of MPP optimization in a reconfigurable manufacturing framework with the help of a reconfigurable multiparts manufacturing flow line.
Originality/value: The results of the case illustration have demonstrated the practical use of diversity control
implemented in the MGATO technique. In comparison to MGAWTO, the implemented MGATO improves the
population diversity through a customized threshold operator. It was clear that the MGATO can obtain better
solution quality by foiling the tendency towards premature convergence
Investigating HLB control strategies using Genetic Algorithms: A two-orchard model approach with ACP Dispersal
This study focuses on the use of genetic algorithms to optimize control
parameters in two potential strategies called mechanical and chemical control,
for mitigating the spread of Huanglongbing (HLB) in citrus orchards. By
developing a two-orchard model that incorporates the dispersal of the Asian
Citrus Psyllid (ACP), the cost functions and objective function are explored to
assess the effectiveness of the proposed control strategies. The mobility of
ACP is also taken into account to capture the disease dynamics more
realistically. Additionally, a mathematical expression for the global
reproduction number () is derived, allowing for sensitivity analysis of
the model parameters when ACP mobility is present. Furthermore, we
mathematically express the cost function and efficiency of the strategy in
terms of the final size and individual of each patch (i.e., when ACP
mobility is absent). The results obtained through the genetic algorithms reveal
optimal parameters for each control strategy, providing valuable insights for
decision-making in implementing effective control measures against HLB in
citrus orchards. This study highlights the importance of optimizing control
parameters in disease management in agriculture and provides a solid foundation
for future research in developing disease control strategies based on genetic
algorithms
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Intelligent Active Vibration Control for a Flexible Beam System
YesThis paper presents an investigation into the
development of an intelligent active vibration control
(AVC) system. Evolutionary Genetic algorithms (GAs)
and Adaptive Neuro-Fuzzy Inference system (ANFIS)
algorithms are used to develop mechanisms of an AVC
system, where the controller is designed on the basis of
optimal vibration suppression using the plant model. A
simulation platform of a flexible beam system in
transverse vibration using finite difference (FD) method
is considered to demonstrate the capabilities of the AVC
system using GAs and ANFIS. MATLAB GA tool box for
GAs and Fuzzy Logic tool box for ANFIS function are
used for AVC system design. The system is then
implemented, tested and its performance assessed for GAs
and ANFIS based design. Finally a comparative
performance of the algorithm in implementing AVC
system using GAs and ANFIS is presented and discussed
through a set of experiments
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