Abstract
Kalman Filters (KF) are at the root of many computational solutions for autonomous systems navigation problems, besides other application domains. The basic linear formulation has been extended in several ways to cope with non-linar dynamic environments. One of the latest trend is to introduce other Computational Intelligence (CI) tools, such as Fuzzy Systems or Artificial Neural Networks inside its computational loop, in order to obtain learning and advanced adaptive properties. This paper offers a short review of current approaches.
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Graña, M., Villaverde, I., Guede, J.M.L., Fernández, B. (2009). Review of Hybridizations of Kalman Filters with Fuzzy and Neural Computing for Mobile Robot Navigation. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_15
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DOI: https://doi.org/10.1007/978-3-642-02319-4_15
Publisher Name: Springer, Berlin, Heidelberg
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