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 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.

[1]  Chia-Feng Juang,et al.  Ant Colony Optimization Algorithm for Fuzzy Controller Design and Its FPGA Implementation , 2008, IEEE Transactions on Industrial Electronics.

[2]  Hani Hagras,et al.  A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots , 2004, IEEE Transactions on Fuzzy Systems.

[3]  Chia-Feng Juang,et al.  A Self-Evolving Interval Type-2 Fuzzy Neural Network With Online Structure and Parameter Learning , 2008, IEEE Transactions on Fuzzy Systems.

[4]  Jerry M. Mendel,et al.  Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters , 2000, IEEE Trans. Fuzzy Syst..

[5]  Jorge Casillas,et al.  Learning Fuzzy Rules Using Ant Colony Optimization Algorithms , 2000 .

[6]  Chia-Feng Juang,et al.  A fuzzified neural fuzzy inference network for handling both linguistic and numerical information simultaneously , 2007, Neurocomputing.

[7]  J. Mendel.  Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .

[8]  Marco Dorigo,et al.  The hyper-cube framework for ant colony optimization , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[9]  Chia-Feng Juang,et al.  A Type-2 Self-Organizing Neural Fuzzy System and Its FPGA Implementation , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[11]  Chia-Feng Juang,et al.  Combination of online clustering and Q-value based GA for reinforcement fuzzy system design , 2005, IEEE Transactions on Fuzzy Systems.

[12]  Chin-Teng Lin,et al.  Genetic Reinforcement Learning through Symbiotic Evolution for Fuzzy Controller Design , 2022 .

[13]  Marco Dorigo,et al.  Ant colony optimization , 2006, IEEE Computational Intelligence Magazine.

[14]  Chia-Feng Juang,et al.  Combination of online clustering and Q-value based GA for reinforcement fuzzy system design , 2005, IEEE Trans. Fuzzy Syst..

[15]  김재현,et al.  Fuzzy-Q learning , 1996 .

[16]  Lionel Jouffe,et al.  Fuzzy inference system learning by reinforcement methods , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[17]  Meng Joo Er,et al.  Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning , 2004, IEEE Trans. Syst. Man Cybern. Part B.