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Erschienen in:
11.03.2016 | Original Article
Extreme learning machine with fuzzy input and fuzzy output for fuzzy regression
Erschienen in: Neural Computing and Applications | Ausgabe 11/2017
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Abstract
It is practically and theoretically significant to approximate and simulate a system with fuzzy inputs and fuzzy outputs. This paper proposes a extreme learning machine (ELM)-based fuzzy regression model (\({{\rm FR}}_{{{\rm ELM}}}\)) in which both inputs and outputs are triangular fuzzy numbers. Algorithm for training \({{\rm FR}}_{{{\rm ELM}}}\) is designed, and its computational complexity is analyzed. Furthermore, the convergence and error estimation for \({{\rm FR}}_{{{\rm ELM}}}\) are discussed. Numerical simulations show that the proposed \({{\rm FR}}_{{{\rm ELM}}}\) can effectively approximate a fuzzy input and fuzzy output system.