Reinforcement Self-Organizing Interval Type-2 Fuzzy System with ant colony optimization

This paper proposes a Reinforcement Self-Organizing Interval Type-2 Fuzzy System with Ant Colony Optimization (RSOIT2FS-ACO) method. The antecedent part in each fuzzy rule of the RSOIT2FS-ACO uses interval type-2 fuzzy sets in order to improve system robustness to noise. There are no fuzzy rules ini...

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Bibliographic Details
Main Authors: Chia-Feng Juang, Chia-Hung Hsu, Chia-Feng Chuang
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Ant colony optimization
Cybernetics
Frequency selective surfaces
Fuzzy control
Fuzzy sets
Fuzzy systems
Noise robustness
reinforcement learning
Supervised learning
type-2 fuzzy systems
USA Councils
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Summary:This paper proposes a Reinforcement Self-Organizing Interval Type-2 Fuzzy System with Ant Colony Optimization (RSOIT2FS-ACO) method. The antecedent part in each fuzzy rule of the RSOIT2FS-ACO uses interval type-2 fuzzy sets in order to improve system robustness to noise. There are no fuzzy rules initially. The RSOIT2FS-ACO generates all rules online. The consequent part of each fuzzy rule is designed using Ant Colony Optimization (ACO). The ACO approach selects the consequent part from a set of candidate actions according to ant pheromone trails. The RSOIT2FS-ACO method is applied to a truck backing control. The proposed RSOIT2FS-ACO is compared with other reinforcement fuzzy systems to verify its efficiency and effectiveness. A comparison with type-1 fuzzy systems verifies the robustness of using type-2 fuzzy systems to noise.
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2009.5346806