Near real-time shadow generation using BSP trees
SIGGRAPH '89 Proceedings of the 16th annual conference on Computer graphics and interactive techniques
Neural networks in designing fuzzy systems for real world applications
Fuzzy Sets and Systems
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
NEFCLASSmdash;a neuro-fuzzy approach for the classification of data
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Hierarchical neuro-fuzzy quadtree models
Fuzzy Sets and Systems - Fuzzy models
Hierarchical Neuro-Fuzzy BSP Model HNFB
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Fuzzy inference system learning by reinforcement methods
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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This work introduces a new class of neuro-fuzzy models called Hierarchical Neuro-Fuzzy BSP Systems (HNFB). These models employ the BSP partitioning (Binary Space Partitioning) of the input space and has been developed in order to bypass the traditional drawbacks of neuro-fuzzy systems: the reduced number of allowed inputs and the poor capacity to create their own structure. First the paper briefly introduces the HNFB model based on supervised learning algorithm. Then it details the RL_HNFB model, which is a hierarchical neuro-fuzzy system with reinforcement learning process. The RL_HNFB model was evaluated in a benchmark application - mountain car - yielding good performance when compared with different reinforcement learning models.