Title :
Reinforcement Self-Organizing Interval Type-2 Fuzzy System with ant colony optimization
Author :
Juang, Chia-Feng ; Hsu, Chia-Hung ; Chuang, Chia-Feng
Author_Institution :
Dept. of Electr. Eng., Nat. Chung-Hsing Univ., Taichung, Taiwan
Abstract :
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.
Keywords :
fuzzy set theory; fuzzy systems; learning (artificial intelligence); optimisation; self-adjusting systems; ant colony optimization; ant pheromone trails; fuzzy rules; interval type-2 fuzzy sets; reinforcement self-organizing interval type-2 fuzzy system; Algorithm design and analysis; Ant colony optimization; Cybernetics; Frequency selective surfaces; Fuzzy control; Fuzzy sets; Fuzzy systems; Noise robustness; Supervised learning; USA Councils; Ant colony optimization; fuzzy control; reinforcement learning; type-2 fuzzy systems;
Conference_Titel :
Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
978-1-4244-2793-2
Electronic_ISBN :
1062-922X
DOI :
10.1109/ICSMC.2009.5346806
