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Model Identification Using Neuro-Fuzzy Approach

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A Hybrid Approach for Power Plant Fault Diagnostics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 743))

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Abstract

This chapter contains the discussion on fundamental concepts related to nonlinear model identification. First, linear in parameter model identification techniques are presented. This covers static and dynamic systems. Following that, the idea of developing nonlinear models in the framework of Orhonormal Basis Functions (OBF) is described. In Sect. 3.3, basic theory of neural networks and fuzzy systems are elaborated. In the state of the art designs, one of them is constructed in the structure of the other allowing the development of a transparent model that can be trained with relatively minimal effort. Section 3.4 is dedicated to the discussion of nonlinear system identification using combined version of neural networks and fuzzy systems. Last section of the chapter deals with three different model training algorithms Least squares based, back-propagation and particle swarm optimization.

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  1. Department of Mechanical Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia

    Tamiru Alemu Lemma

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Lemma, T.A. (2018). Model Identification Using Neuro-Fuzzy Approach. In: A Hybrid Approach for Power Plant Fault Diagnostics. Studies in Computational Intelligence, vol 743. Springer, Cham. https://doi.org/10.1007/978-3-319-71871-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-71871-2_3

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