Beschreibung
Nowadays, fuzzy differential equation FDE is a popular topic studied by many researchers since it is utilized widely for the purpose of modeling problems in science and engineering. Most of the practical problems require the solution of FDE which satisfies fuzzy initial or fuzzy boundary conditions, therefore, the fuzzy problem should be solved. However, many FDE could not be solved exactly, sometimes it is even impossible to find their analytical solutions. Thus, considering their approximate solutions is becoming more important. In this work, for solving FDE Under Generalized H - Derivation, we present modified numerical method which relies on the function approximation capabilities of artificial neural network (ANN) and results in the construction of a solution written in a differentiable, closed analytic form. This form employs ANN as the basic approximation element, whose parameters weights and biases are adjusted to minimize an appropriate error function. This method can result in improved numerical methods for solving FDE.
Autorenporträt
Dr. Mazin H. Suhhiem was born in the city of Alrifaee in 1980.He obtained his Ph.D degree in applied mathematics in 2016 and Published many researches that combine numerical methods and the fuzzy theory. Currently he belongs into the teaching staff at university of Sumer, South of Iraq.
Herstellerkennzeichnung:
OmniScriptum SRL
Str. Armeneasca 28/1, office 1
2012 Chisinau
MD
E-Mail: info@omniscriptum.com
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