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TFS
2011
239views Education» more  TFS 2011»
13 years 3 months ago
Systems Control With Generalized Probabilistic Fuzzy-Reinforcement Learning
—Reinforcement learning (RL) is a valuable learning method when the systems require a selection of control actions whose consequences emerge over long periods for which input– ...
William M. Hinojosa, Samia Nefti, Uzay Kaymak
IJCSS
2000
176views more  IJCSS 2000»
13 years 8 months ago
AI-based discrete sliding-mode control for real-time implementation
Combination of sliding-mode control and an AI block based on fuzzy logic is introduced. This hybrid control structure provides very good control performance and robustness of contr...
Vladimir B. Bajic, Daohang Sha, Xinzhong Li, Xingm...
TFS
2008
90views more  TFS 2008»
13 years 8 months ago
Fuzzy Interpolation and Extrapolation: A Practical Approach
Fuzzy interpolation does not only help to reduce the complexity of fuzzy models, but also makes inference in sparse rule-based systems possible. It has been successfully applied to...
Zhiheng Huang, Qiang Shen
SOCO
2002
Springer
13 years 8 months ago
A dynamically-constructed fuzzy neural controller for direct model reference adaptive control of multi-input-multi-output nonlin
Conventional industrial control systems are in majority based on the single-input-single-output design principle with linearized models of the processes. However, most industrial p...
Yakov Frayman, Lipo Wang
TSMC
1998
139views more  TSMC 1998»
13 years 8 months ago
A neuro-fuzzy controller for mobile robot navigation and multirobot convoying
—A Neural integrated Fuzzy conTroller (NiF-T) which integrates the fuzzy logic representation of human knowledge with the learning capability of neural networks is developed for ...
Kim C. Ng, Mohan M. Trivedi