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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2017

08.03.2016

A Neural Network Approach for Solving a Class of Fractional Optimal Control Problems

verfasst von: Javad Sabouri K., Sohrab Effati, Morteza Pakdaman

Erschienen in: Neural Processing Letters | Ausgabe 1/2017

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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.
Vorheriger Artikel Hyperconic Multilayer Perceptron
Nächster Artikel Multi-focus Image Fusion Based on the Improved PCNN and Guided Filter

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Metadaten
Titel
A Neural Network Approach for Solving a Class of Fractional Optimal Control Problems
verfasst von
Javad Sabouri K.
Sohrab Effati
Morteza Pakdaman
Publikationsdatum
08.03.2016
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2017
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-016-9510-5

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