Title
A Neural Network Approach for Solving a Class of Fractional Optimal Control Problems.
Abstract
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.
Year
DOI
Venue
2017
https://doi.org/10.1007/s11063-016-9510-5
Neural Processing Letters
Keywords
Field
DocType
Fractional optimal control problem,Volterra integral equation,Perceptron neural networks,Unconstrained optimization
Boundary value problem,Mathematical optimization,Optimal control,Nonlinear system,Artificial neural network,Perceptron,Mathematics,Volterra integral equation
Journal
Volume
Issue
ISSN
45
1
1370-4621
Citations 
PageRank 
References 
14
0.55
10
Authors
3
Name
Order
Citations
PageRank
Javad Sabouri K.1140.55
Effati Sohrab227630.31
Morteza Pakdaman3998.29