Neural Processing Letters, ( ISI ), Volume (1), No (1), Year (2016-3) , Pages (1-15)

Title : ( A Neural Network Approach for Solving a Class of Fractional Optimal Control Problems )

Authors: javad sabouri , Sohrab Effati , Morteza Pakdaman ,

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In this paper the perceptron neural networks are applied to approximate the solution of fractional optimal control problems. The necessary (and also sufficient in most cases) optimality conditions are stated in a form of fractional two-point boundary value problem. Then this problem is converted to a Volterra integral equation. By using perceptron neural network’s ability in approximating a nonlinear function, first we propose approximating functions to estimate control, state and co-state functions which they satisfy the initial or boundary conditions. The approximating functions contain neural network with unknown weights. Using an optimization approach, the weights are adjusted such that the approximating functions satisfy the optimality conditions of fractional optimal control problem. Numerical results illustrate the advantages of the method.

Keywords

Fractional optimal control problem · Volterra integral equation · Perceptron neural networks · Unconstrained
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@article{paperid:1056828,
author = {Sabouri, Javad and Effati, Sohrab and Pakdaman, Morteza},
title = {A Neural Network Approach for Solving a Class of Fractional Optimal Control Problems},
journal = {Neural Processing Letters},
year = {2016},
volume = {1},
number = {1},
month = {March},
issn = {1370-4621},
pages = {1--15},
numpages = {14},
keywords = {Fractional optimal control problem · Volterra integral equation · Perceptron neural networks · Unconstrained optimization},
}

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%0 Journal Article
%T A Neural Network Approach for Solving a Class of Fractional Optimal Control Problems
%A Sabouri, Javad
%A Effati, Sohrab
%A Pakdaman, Morteza
%J Neural Processing Letters
%@ 1370-4621
%D 2016

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