Summary
This chapter is devoted to present two adaptive fuzzy filters with online structure and parameter learning ability with an important feature that they can dynamically partition the input and output spaces using a modified FCM (Fuzzy CMeans) clustering algorithm according to the input-output data distribution. These filters are also able to tune membership functions and find fuzzy logic rules in an on-line manner. This chapter also introduces a new evolutionary algorithm called OGA as a learning system for the adaptive fuzzy filters. Finally the adaptive fuzzy filters are applied to maneuvering target tracking problem and their performance is compared with that of the classical Kalman Based techniques (Interacting Multiple Model).
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Menhaj, M.B. (2003). Adaptive Fuzzy Filters and Their Application to Online Maneuvering Target Tracking. In: Nachtegael, M., Van der Weken, D., Kerre, E.E., Van De Ville, D. (eds) Fuzzy Filters for Image Processing. Studies in Fuzziness and Soft Computing, vol 122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36420-7_13
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DOI: https://doi.org/10.1007/978-3-540-36420-7_13
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