Artificial neural network approach for solving fuzzy differential equations

  • Authors:

  • Sohrab Effati;Morteza Pakdaman

  • Affiliations:

  • Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran;Sama Organization (affiliated with Islamic Azad University)-Mashhad Branch, Mashhad, Iran

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2010

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Abstract

The current research attempts to offer a novel method for solving fuzzy differential equations with initial conditions based on the use of feed-forward neural networks. First, the fuzzy differential equation is replaced by a system of ordinary differential equations. A trial solution of this system is written as a sum of two parts. The first part satisfies the initial condition and contains no adjustable parameters. The second part involves a feed-forward neural network containing adjustable parameters (the weights). Hence by construction, the initial condition is satisfied and the network is trained to satisfy the differential equations. This method, in comparison with existing numerical methods, shows that the use of neural networks provides solutions with good generalization and high accuracy. The proposed method is illustrated by several examples.