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Indirect Adaptive Control with Fuzzy Neural Networks via Kernel Smoothing

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Advances in Computational Intelligence (MICAI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7630))

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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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Author information

Authors and Affiliations

  1. Tecnológico de Estudios Superiores de Coacalco, Unidad de Estudios de Posgrado e Investigación, Mexico

    Israel Cruz Vega & Luis Moreno-Ahedo

  2. Automatic Control Deparment, CINVESTAV, Mexico

    Wen Yu Liu

Authors
  1. Israel Cruz Vega
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  2. Luis Moreno-Ahedo
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  3. Wen Yu Liu
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Editor information

Editors and Affiliations

  1. Mexican Petroleum Institute, Eje Central Lazaro Cardenas Norte, 152, Col. San Bartolo Atepehuacan, CP 07730, México D.F., Mexico

    Ildar Batyrshin

  2. Tecnológico de Monterrey, Campus Estado de México, Carretera Lago de Guadalupe Km 3.5, CP 52926, Atizapán de Zaragoza, Estado de México, Mexico

    Miguel González Mendoza

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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

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37797-6

  • Online ISBN: 978-3-642-37798-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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