Machine Learning
Dynamic Self-Generated Fuzzy Systems for Reinforcement Learning
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
Fuzzy inference system learning by reinforcement methods
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks
IEEE Transactions on Fuzzy Systems
Combination of online clustering and Q-value based GA for reinforcement fuzzy system design
IEEE Transactions on Fuzzy Systems
Adaptive noise cancellation using enhanced dynamic fuzzy neural networks
IEEE Transactions on Fuzzy Systems
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In this paper, a novel hybrid self-learning approach termed Enhanced Dynamic Self-Generated Fuzzy Q-Learning (EDSGFQL) for automatically generating a Fuzzy Inference System (FIS) is presented. In the EDSGFQL approach, the structure of an FIS is generated via Reinforcement Learning (RL) while the centers of Membership Functions (MFs) are updated by an extended Self Organizing Map (SOM) algorithm. The proposed EDSGFQL methodology can automatically create, delete and adjust fuzzy rules without any priori knowledge. In the EDSGFQL approach, fuzzy rules of an FIS are regarded as agents of the entire system and all of the rules are recruited, adjusted and terminated according to their contributions and participation. At the mean time, the centers of MFs are adjusted to move to the real centers in the sense of feature representation by the extended SOM approach. Comparative studies on a wall-following task by a mobile robot have been done for the proposed EDSGFQL approach and other current methodologies and the demonstration results show that the proposed EDSGFQL approach is superior.