Documents disponibles écrits par cet auteur

Reinforcement ant optimized fuzzy controller for mobile-robot wall-following control / Juang, Chia-Feng in IEEE transactions on industrial electronics, Vol. 56 N° 10 (Octobre 2009) 

Public ISBD [article]  in IEEE transactions on industrial electronics > Vol. 56 N° 10 (Octobre 2009) . - pp. 3931 - 3940 Titre : Reinforcement ant optimized fuzzy controller for mobile-robot wall-following control Type de document : texte imprimé Auteurs : Juang, Chia-Feng, Auteur ; Hsu, Chia-Hung, Auteur Article en page(s) : pp. 3931 - 3940 Note générale : Génie électrique Langues : Anglais (eng) Mots-clés : Ant  colony  optimization  (ACO)  Fuzzy  Q-learning  Reinforced  learning  Robot  motion  control  Type-2  fuzzy  systems Index. décimale : 621.38 Dispositifs électroniques. Tubes à électrons. Photocellules. Accélérateurs de particules. Tubes à rayons X Résumé : This paper proposes a reinforcement ant optimized fuzzy controller (FC) design method, called RAOFC, and applies it to wheeled-mobile-robot wall-following control under reinforcement learning environments. The inputs to the designed FC are range-finding sonar sensors, and the controller output is a robot steering angle. The antecedent part in each fuzzy rule uses interval type-2 fuzzy sets in order to increase FC robustness. No a priori assignment of fuzzy rules is necessary in RAOFC. An online aligned interval type-2 fuzzy clustering (AIT2FC) method is proposed to generate rules automatically. The AIT2FC not only flexibly partitions the input space but also reduces the number of fuzzy sets in each input dimension, which improves controller interpretability. The consequent part of each fuzzy rule is designed using Q-value aided ant colony optimization (QACO). The QACO approach selects the consequent part from a set of candidate actions according to ant pheromone trails and Q-values, both of whose values are updated using reinforcement signals. Simulations and experiments on mobile-robot wall-following control show the effectiveness and efficiency of the proposed RAOFC. DEWEY : 621.38 ISSN : 0278-0046 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4804797 [article] Reinforcement ant optimized fuzzy controller for mobile-robot wall-following control [texte imprimé] / Juang, Chia-Feng, Auteur ; Hsu, Chia-Hung, Auteur . - pp. 3931 - 3940. Génie électrique Langues : Anglais (eng) in IEEE transactions on industrial electronics > Vol. 56 N° 10 (Octobre 2009) . - pp. 3931 - 3940 Mots-clés : Ant  colony  optimization  (ACO)  Fuzzy  Q-learning  Reinforced  learning  Robot  motion  control  Type-2  fuzzy  systems Index. décimale : 621.38 Dispositifs électroniques. Tubes à électrons. Photocellules. Accélérateurs de particules. Tubes à rayons X Résumé : This paper proposes a reinforcement ant optimized fuzzy controller (FC) design method, called RAOFC, and applies it to wheeled-mobile-robot wall-following control under reinforcement learning environments. The inputs to the designed FC are range-finding sonar sensors, and the controller output is a robot steering angle. The antecedent part in each fuzzy rule uses interval type-2 fuzzy sets in order to increase FC robustness. No a priori assignment of fuzzy rules is necessary in RAOFC. An online aligned interval type-2 fuzzy clustering (AIT2FC) method is proposed to generate rules automatically. The AIT2FC not only flexibly partitions the input space but also reduces the number of fuzzy sets in each input dimension, which improves controller interpretability. The consequent part of each fuzzy rule is designed using Q-value aided ant colony optimization (QACO). The QACO approach selects the consequent part from a set of candidate actions according to ant pheromone trails and Q-values, both of whose values are updated using reinforcement signals. Simulations and experiments on mobile-robot wall-following control show the effectiveness and efficiency of the proposed RAOFC. DEWEY : 621.38 ISSN : 0278-0046 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4804797

Water Bath Temperature Control by a Recurrent Fuzzy Controller and its FPGA Implementation / Juang, Chia-Feng in IEEE transactions on industrial electronics, Vol. 53 N° 3 (Juin 2006) 

Public ISBD [article]  in IEEE transactions on industrial electronics > Vol. 53 N° 3 (Juin 2006) . - 941- 949 p. Titre : Water Bath Temperature Control by a Recurrent Fuzzy Controller and its FPGA Implementation Titre original : Commande de Température de Bain d'Eau par un Contrôleur Brouillé Récurrent et son Exécution de FPGA Type de document : texte imprimé Auteurs : Juang, Chia-Feng, Auteur ; Chen, Jung-Shing, Auteur Article en page(s) : 941- 949 p. Note générale : Génie Electrique Langues : Anglais (eng) Mots-clés : Direct  inverse  control  Fuzzy  chip  Fuzzy  control  Neural-network  Structure/parameter  learning.Commande  inverse  directe  Morceau  brouillé  Commande  brouillée  Réseau  neurologique  Etude  de  paramètre  de  structure. Index. décimale : 621 Ingénierie mécanique en général. Technologie nucléaire. Ingénierie électrique. Machinerie Résumé : A hardware implementation of the Takagi-Sugeno-Kan (TSK)-type recurrent fuzzy network (TRFN-H) for water bath temperature control is proposed in this paper. The TRFN-H is constructed by a series of recurrent fuzzy if-then rules built on-line through concurrent structure and parameter learning. To design TRFN-H for temperature control, the direct inverse control configuration is adopted, and owing to the structure of TRFN-H, no a priori knowledge of the plant order is required, which eases the design process. Due to the powerful learning ability of TRFN-H, a small network is generated, which significantly reduces the hardware implementation cost. After the network is designed, it is realized on a field-programmable gate array (FPGA) chip. Because both the rule and input variable numbers in TRFN-H are small, it is implemented by combinational circuits directly without using any memory. The good performance of the TRFN-H chip is verified from comparisons with computer-based proportional-integral fuzzy (PI) and neural network controllers for different sets of experiments on water bath temperature control. DEWEY : 621 ISSN : 0278-0046 En ligne : cfjuang@dragon.nchu.edu.tw, jschen@magicpixel.com.tw [article] Water Bath Temperature Control by a Recurrent Fuzzy Controller and its FPGA Implementation = Commande de Température de Bain d'Eau par un Contrôleur Brouillé Récurrent et son Exécution de FPGA [texte imprimé] / Juang, Chia-Feng, Auteur ; Chen, Jung-Shing, Auteur . - 941- 949 p. Génie Electrique Langues : Anglais (eng) in IEEE transactions on industrial electronics > Vol. 53 N° 3 (Juin 2006) . - 941- 949 p. Mots-clés : Direct  inverse  control  Fuzzy  chip  Fuzzy  control  Neural-network  Structure/parameter  learning.Commande  inverse  directe  Morceau  brouillé  Commande  brouillée  Réseau  neurologique  Etude  de  paramètre  de  structure. Index. décimale : 621 Ingénierie mécanique en général. Technologie nucléaire. Ingénierie électrique. Machinerie Résumé : A hardware implementation of the Takagi-Sugeno-Kan (TSK)-type recurrent fuzzy network (TRFN-H) for water bath temperature control is proposed in this paper. The TRFN-H is constructed by a series of recurrent fuzzy if-then rules built on-line through concurrent structure and parameter learning. To design TRFN-H for temperature control, the direct inverse control configuration is adopted, and owing to the structure of TRFN-H, no a priori knowledge of the plant order is required, which eases the design process. Due to the powerful learning ability of TRFN-H, a small network is generated, which significantly reduces the hardware implementation cost. After the network is designed, it is realized on a field-programmable gate array (FPGA) chip. Because both the rule and input variable numbers in TRFN-H are small, it is implemented by combinational circuits directly without using any memory. The good performance of the TRFN-H chip is verified from comparisons with computer-based proportional-integral fuzzy (PI) and neural network controllers for different sets of experiments on water bath temperature control. DEWEY : 621 ISSN : 0278-0046 En ligne : cfjuang@dragon.nchu.edu.tw, jschen@magicpixel.com.tw