—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– ...
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...
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...
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...
—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 ...