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A parametric recurrent neural network scheme for solving a class of fuzzy regression models with some real-world applications

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Abstract

In this paper, a hybrid scheme based on recurrent neural networks for approximate fuzzy coefficients (parameters) of fuzzy linear and polynomial regression models with fuzzy output and crisp inputs is presented. Here, a neural network is first constructed based on some concepts of convex optimization and stability theory. The suggested neural network model guarantees to find the approximate parameters of the fuzzy regression problem. The existence and convergence of the trajectories of the neural network are studied. The Lyapunov stability for the neural network is also shown. Some illustrative examples provide a further demonstration of the effectiveness of the method.

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  1. Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran

    Delara Karbasi, Alireza Nazemi & Mohammadreza Rabiei

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  1. Delara Karbasi
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  2. Alireza Nazemi
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  3. Mohammadreza Rabiei
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Correspondence to Alireza Nazemi.

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Karbasi, D., Nazemi, A. & Rabiei, M. A parametric recurrent neural network scheme for solving a class of fuzzy regression models with some real-world applications. Soft Comput 24, 11159–11187 (2020). https://doi.org/10.1007/s00500-020-05008-1

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