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Found 4 papers, 1 papers with code
Efficient Learning of Fuzzy Logic Systems for Large-Scale Data Using Deep Learning
Type-1 and Interval Type-2 (IT2) Fuzzy Logic Systems (FLS) excel in handling uncertainty alongside their parsimonious rule-based structure.
Enhancing Interval Type-2 Fuzzy Logic Systems: Learning for Precision and Prediction Intervals
In this context, we first provide extra design flexibility to the Karnik-Mendel (KM) and Nie-Tan (NT) center of sets calculation methods to increase their flexibility for generating PIs.
Zadeh's Type-2 Fuzzy Logic Systems: Precision and High-Quality Prediction Intervals
After detailing the construction of Z-GT2-FLSs, we provide solutions to challenges while learning from high-dimensional data: the curse of dimensionality, and integrating Deep Learning (DL) optimizers.
A New Approach for Tactical Decision Making in Lane Changing: Sample Efficient Deep Q Learning with a Safety Feedback Reward
Automated lane change is one of the most challenging task to be solved of highly automated vehicles due to its safety-critical, uncertain and multi-agent nature.
