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Evaluation of prediction error based fuzzy model clustering approaches for multiple model learning

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Abstract

Identifying multiple models from both static and dynamic data is an important problem in several engineering fields. Clustering based on Euclidean distance measure has been proposed to solve this problem. However, since Euclidean distance is not directly related to model fidelity, these approaches can lead to suboptimal results even when the number of models is known. In this work, through a three step algorithm that includes initialization, prediction error based fuzzy clustering and model rationalization, we evaluate the possibility of uncovering multiple model structures from data. The three step algorithm is also assessed for the identification of piecewise auto regressive exogenous systems with unknown number of models and their (unknown)orders. The basic approach can be extended for trend analysis and generalized principal component analysis.

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Authors and Affiliations

  1. Department of Chemical Engineering, Texas Tech University, Lubbock, TX, 79409, USA

    Vidyashankar Kuppuraj & Raghunathan Rengaswamy

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  1. Vidyashankar Kuppuraj
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  2. Raghunathan Rengaswamy
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Correspondence to Raghunathan Rengaswamy.

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Kuppuraj, V., Rengaswamy, R. Evaluation of prediction error based fuzzy model clustering approaches for multiple model learning. Int J Adv Eng Sci Appl Math 4, 10–21 (2012). https://doi.org/10.1007/s12572-012-0058-y

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