The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
L'article traite de la méthode d'estimation des équations système du comportement dynamique d'un mécanisme de tarification des intrants en utilisant la programmation génétique (GP) et ses applications. Le schéma est similaire à la méthode récente de réduction du bruit dans la parole bruyante qui est basée sur le traitement adaptatif du signal numérique pour l'identification du système et la soustraction du bruit estimé. Nous considérons le comportement dynamique d'un mécanisme de tarification des intrants pour une installation de service dans lequel des clients hétérogènes auto-optimisés fondent leurs futures décisions d'adhésion/refus sur leurs expériences antérieures de congestion. Dans le GP, les équations du système sont représentées par des arbres d'analyse et la performance (aptitude) de chaque individu est définie comme l'inversion de l'erreur quadratique moyenne entre les données observées et la sortie de l'équation du système. En sélectionnant une paire d’individus ayant une meilleure forme physique, l’opération de croisement est appliquée pour générer de nouveaux individus. La chaîne utilisée pour le GP est étendue pour traiter la forme rationnelle des fonctions du système. La condition du chaos de Li-Yorke est exploitée pour assurer la chaoticité des fonctions approximées. Sous notre contrôle, puisque les équations du système sont estimées, il nous suffit de modifier l'entrée progressivement pour que le système se déplace vers la région stable. En supposant le système dynamique ciblé f(x(t)) avec entrée u(t)=0 est estimé en utilisant le GP (noté
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Xiaorong CHEN, Shozo TOKINAGA, "Approximation of Chaotic Dynamics for Input Pricing at Service Facilities Based on the GP and the Control of Chaos" in IEICE TRANSACTIONS on Fundamentals,
vol. E85-A, no. 9, pp. 2107-2117, September 2002, doi: .
Abstract: The paper deals with the estimation method of system equations of dynamic behavior of an input-pricing mechanism by using the Genetic Programming (GP) and its applications. The scheme is similar to recent noise reduction method in noisy speech which is based on the adaptive digital signal processing for system identification and subtraction estimated noise. We consider the dynamic behavior of an input-pricing mechanism for a service facility in which heterogeneous self-optimizing customers base their future join/balk decisions on their previous experiences of congestion. In the GP, the system equations are represented by parse trees and the performance (fitness) of each individual is defined as the inversion of the root mean square error between the observed data and the output of the system equation. By selecting a pair of individuals having higher fitness, the crossover operation is applied to generate new individuals. The string used for the GP is extended to treat the rational form of system functions. The condition for the Li-Yorke chaos is exploited to ensure the chaoticity of the approximated functions. In our control, since the system equations are estimated, we only need to change the input incrementally so that the system moves to the stable region. By assuming the targeted dynamic system f(x(t)) with input u(t)=0 is estimated by using the GP (denoted
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e85-a_9_2107/_p
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@ARTICLE{e85-a_9_2107,
author={Xiaorong CHEN, Shozo TOKINAGA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Approximation of Chaotic Dynamics for Input Pricing at Service Facilities Based on the GP and the Control of Chaos},
year={2002},
volume={E85-A},
number={9},
pages={2107-2117},
abstract={The paper deals with the estimation method of system equations of dynamic behavior of an input-pricing mechanism by using the Genetic Programming (GP) and its applications. The scheme is similar to recent noise reduction method in noisy speech which is based on the adaptive digital signal processing for system identification and subtraction estimated noise. We consider the dynamic behavior of an input-pricing mechanism for a service facility in which heterogeneous self-optimizing customers base their future join/balk decisions on their previous experiences of congestion. In the GP, the system equations are represented by parse trees and the performance (fitness) of each individual is defined as the inversion of the root mean square error between the observed data and the output of the system equation. By selecting a pair of individuals having higher fitness, the crossover operation is applied to generate new individuals. The string used for the GP is extended to treat the rational form of system functions. The condition for the Li-Yorke chaos is exploited to ensure the chaoticity of the approximated functions. In our control, since the system equations are estimated, we only need to change the input incrementally so that the system moves to the stable region. By assuming the targeted dynamic system f(x(t)) with input u(t)=0 is estimated by using the GP (denoted
keywords={},
doi={},
ISSN={},
month={September},}
Copier
TY - JOUR
TI - Approximation of Chaotic Dynamics for Input Pricing at Service Facilities Based on the GP and the Control of Chaos
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2107
EP - 2117
AU - Xiaorong CHEN
AU - Shozo TOKINAGA
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E85-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2002
AB - The paper deals with the estimation method of system equations of dynamic behavior of an input-pricing mechanism by using the Genetic Programming (GP) and its applications. The scheme is similar to recent noise reduction method in noisy speech which is based on the adaptive digital signal processing for system identification and subtraction estimated noise. We consider the dynamic behavior of an input-pricing mechanism for a service facility in which heterogeneous self-optimizing customers base their future join/balk decisions on their previous experiences of congestion. In the GP, the system equations are represented by parse trees and the performance (fitness) of each individual is defined as the inversion of the root mean square error between the observed data and the output of the system equation. By selecting a pair of individuals having higher fitness, the crossover operation is applied to generate new individuals. The string used for the GP is extended to treat the rational form of system functions. The condition for the Li-Yorke chaos is exploited to ensure the chaoticity of the approximated functions. In our control, since the system equations are estimated, we only need to change the input incrementally so that the system moves to the stable region. By assuming the targeted dynamic system f(x(t)) with input u(t)=0 is estimated by using the GP (denoted
ER -