Solution of Stiff Differential Equations & Dynamical Systems Using Neural Network Methods

نویسندگان

  • Rootvesh Mehta
  • Sandeep Malhotra
چکیده

There are many laws stating the behavior of the physical world involves quantities which are changing with respect to another quantities and the mathematical expressions of such laws are differential equations. There are numbers of problems of various fields like science, engineering-technology, social science etc. involves differential equations. The researchers have developed many analytical and numerical methods for solving various types of differential equations but difficulties are aroused when stiff differential equations as mathematical models are formed. Stiff differential equations are difficult and complex problems to solve analytically and numerically. Many numerical schemes employing digital computers are available for solving stiff ordinary differential equations, partial differential equations and system of stiff differential equations but have revealed many limitations. In this work we have proposed the artificial neural network method for solving such problems of stiff differential equations as the advance trend.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Optimization of solution stiff differential equations using MHAM and RSK methods

In this paper, a nonlinear stiff differential equation is solved by using the Rosenbrock iterative method, modified homotpy analysis method and power series method. The approximate solution of this equation is calculated in the form of series which its components are computed by applying a recursive relations. Some numerical examples are studied to demonstrate the accuracy of the presented meth...

متن کامل

Numerical solution of fuzzy differential equations under generalized differentiability by fuzzy neural network

In this paper, we interpret a fuzzy differential equation by using the strongly generalized differentiability concept. Utilizing the Generalized characterization Theorem. Then a novel hybrid method based on learning algorithm of fuzzy neural network for the solution of differential equation with fuzzy initial value is presented. Here neural network is considered as a part of large eld called ne...

متن کامل

A hybrid method with optimal stability properties for the numerical solution of stiff differential systems

In this paper, we consider the construction of a new class of numerical methods based on the backward differentiation formulas (BDFs) that be equipped by including two off--step points. We represent these methods from general linear methods (GLMs) point of view which provides an easy process to improve their stability properties and implementation in a variable stepsize mode. These superioritie...

متن کامل

Numerical solution of hybrid fuzzy differential equations by fuzzy neural network

The hybrid fuzzy differential equations have a wide range of applications in science and engineering. We consider the problem of nding their numerical solutions by using a novel hybrid method based on fuzzy neural network. Here neural network is considered as a part of large eld called neural computing or soft computing. The proposed algorithm is illustrated by numerical examples and the result...

متن کامل

Fractional dynamical systems: A fresh view on the local qualitative theorems

The aim of this work is to describe the qualitative behavior of the solution set of a given system of fractional differential equations and limiting behavior of the dynamical system or flow defined by the system of fractional differential equations. In order to achieve this goal, it is first necessary to develop the local theory for fractional nonlinear systems. This is done by the extension of...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017