Abstract
In this paper, a revisited approach for possibilistic fuzzy regression methods is proposed. Indeed, a new modified fuzzy linear model form is introduced where the identified model output can envelop all the observed data and ensure a total inclusion property. Moreover, this model output can have any kind of spread tendency. In this framework, the identification problem is reformulated according to a new criterion that assesses the model fuzziness independently of the collected data. The proposed concepts are used in a global identification process in charge of building a piecewise model able to represent every kinds of output evolution.
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Bisserier, A., Boukezzoula, R., Galichet, S. (2010). Linear Fuzzy Regression Using Trapezoidal Fuzzy Intervals. In: Bouchon-Meunier, B., Magdalena, L., Ojeda-Aciego, M., Verdegay, JL., Yager, R.R. (eds) Foundations of Reasoning under Uncertainty. Studies in Fuzziness and Soft Computing, vol 249. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10728-3_1
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DOI: https://doi.org/10.1007/978-3-642-10728-3_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10726-9
Online ISBN: 978-3-642-10728-3
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