A LAGRANGE NEURAL NETWORK FOR NETWORK TRAFFIC ASSIGNMENT OPTIMIZATION

Year 2023, , 82 - 95, 29.12.2023

Hasan Dalman

https://doi.org/10.51477/mejs.1272077

Abstract

This study considers a nonlinear optimization problem used to achieve user equilibrium in the network traffic assignment problem. By providing the Karush Kuhn Tucker conditions of this optimization problem, it is converted into a system of differential equations using the Lagrange function. This system is then redefined as a Lagrange neural network, which is proven to be asymptotically and lyapunov stable. Finally, a numerical method are used to demonstrate that the results obtained from this neural network are a solution to the optimization problem and converge to user equilibrium.

Keywords

Network traffic assignment, graph theory, dynamical systems, lagrange neural network

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