Abstract
In this paper, a neurofuzzy adaptive control framework for discrete-time systems based on kernel smoothing regression is developed. Kernel regression is a nonparametric statistics technique used to determine a regression model where no model assumption has been done. Due to similarity with fuzzy systems, kernel smoothing is used to obtain knowledge about the structure of the fuzzy system and this information is used as initial conditions of the adaptive neurofuzzy control. Results of simulation shows the efficiency of this technique
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Vega, I.C., Moreno-Ahedo, L., Liu, W.Y. (2013). Indirect Adaptive Control with Fuzzy Neural Networks via Kernel Smoothing. In: Batyrshin, I., Mendoza, M.G. (eds) Advances in Computational Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37798-3_34
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DOI: https://doi.org/10.1007/978-3-642-37798-3_34
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