Research article

Fuzzy gradient descent for the linear fuzzy real number system

  • Received: 11 July 2019 Accepted: 31 July 2019 Published: 09 August 2019
  • MSC : 00A69

  • Many problems in education, finance, and engineering design require that decisions be made under uncertainty. In these fields, Machine Learning is often used to search for patterns and information from data. To find patterns in Fuzzy Data, Fuzzy Machine Learning techniques can be used. In this paper, we focus on solving and manipulating Fuzzy Nonlinear problems in the Linear Fuzzy Real (LFR) number system using the Gradient Descent. The Gradient Descent is the most often used learning algorithm in Machine Learning. Thus, we propose the LFR Gradient Descent method for solving nonlinear equations in the LFR number system.

    Citation: Frank Rogers. Fuzzy gradient descent for the linear fuzzy real number system[J]. AIMS Mathematics, 2019, 4(4): 1078-1086. doi: 10.3934/math.2019.4.1078

    Related Papers:

  • Many problems in education, finance, and engineering design require that decisions be made under uncertainty. In these fields, Machine Learning is often used to search for patterns and information from data. To find patterns in Fuzzy Data, Fuzzy Machine Learning techniques can be used. In this paper, we focus on solving and manipulating Fuzzy Nonlinear problems in the Linear Fuzzy Real (LFR) number system using the Gradient Descent. The Gradient Descent is the most often used learning algorithm in Machine Learning. Thus, we propose the LFR Gradient Descent method for solving nonlinear equations in the LFR number system.
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    © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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