Abstract
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.
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Vellasco, M., Pacheco, M., Figueiredo, K. (2003). Hierarchical Neuro-Fuzzy Systems. In: Mira, J., Álvarez, J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44868-3_17
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DOI: https://doi.org/10.1007/3-540-44868-3_17
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