Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractThe profile in polyphenols and volatile compounds in alcoholic beverages from different cultivars of mulberry    Next AbstractSexual dimorphism of gnathocoxal glands of the spider Leptyphantes sanctivincentii »

IEEE Trans Syst Man Cybern B Cybern


Title:Reinforcement interval type-2 fuzzy controller design by online rule generation and q-value-aided ant colony optimization
Author(s):Juang CF; Hsu CH;
Address:"Department of Electrical Engineering, National Chung-Hsing University, Taichung, Taiwan. cfjuang@dragon.nchu.edu.tw"
Journal Title:IEEE Trans Syst Man Cybern B Cybern
Year:2009
Volume:20090527
Issue:6
Page Number:1528 - 1542
DOI: 10.1109/TSMCB.2009.2020569
ISSN/ISBN:1941-0492 (Electronic) 1083-4419 (Linking)
Abstract:"This paper proposes a new reinforcement-learning method using online rule generation and Q-value-aided ant colony optimization (ORGQACO) for fuzzy controller design. The fuzzy controller is based on an interval type-2 fuzzy system (IT2FS). The antecedent part in the designed IT2FS uses interval type-2 fuzzy sets to improve controller robustness to noise. There are initially no fuzzy rules in the IT2FS. The ORGQACO concurrently designs both the structure and parameters of an IT2FS. We propose an online interval type-2 rule generation method for the evolution of system structure and flexible partitioning of the input space. Consequent part parameters in an IT2FS are designed using Q -values and the reinforcement local-global ant colony optimization algorithm. This algorithm selects the consequent part from a set of candidate actions according to ant pheromone trails and Q-values, both of which are updated using reinforcement signals. The ORGQACO design method is applied to the following three control problems: 1) truck-backing control; 2) magnetic-levitation control; and 3) chaotic-system control. The ORGQACO is compared with other reinforcement-learning methods to verify its efficiency and effectiveness. Comparisons with type-1 fuzzy systems verify the noise robustness property of using an IT2FS"
Keywords:
Notes:"PubMed-not-MEDLINEJuang, Chia-Feng Hsu, Chia-Hung eng Research Support, Non-U.S. Gov't 2009/06/02 IEEE Trans Syst Man Cybern B Cybern. 2009 Dec; 39(6):1528-42. doi: 10.1109/TSMCB.2009.2020569. Epub 2009 May 27"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 27-12-2024