Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regression: Simplifying Complex T2F Calculations
Assef Zare, Afshin Shoeibi, Narges Shafaei Bajestani, Parisa Moridian, Roohallah Alizadehsani, Majid Hallaji, Abbas Khosravi
Abstract
Many studies have been performed to handle the uncertainties in the data using type-1 fuzzy regression (FR). Few type-2 fuzzy (T2F) regression studies have used interval type-2 (IT2) for indeterminate modeling using type-1 fuzzy membership. The current article proposes a triangular T2F regression (TT2FR) model to ameliorate the efficiency of the model by handling the uncertainty in the data. The triangular secondary membership function is used instead of widely used interval-type models. In the proposed model, vagueness in primary and secondary fuzzy sets is minimized, and also, a specified <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> -plane of the observed value is included in the same <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\alpha}$</tex-math></inline-formula> -plane of the predicted value. Complex calculations of the T2F model are simplified by reducing the 3D T2F set into 2D IT2 fuzzy models.