Hopfield model for shortest path computation and routing in ATM network

Author

• Chee Weng Lee, Universiti Malaya

Date of Award

1-1-2001

Thesis Type

Masters

Document Type

Thesis

Divisions

Faculty of Computer Science & Information Technology

Department

-

Institution

Universiti Malaya

Abstract

This research focuses on the application of Hopfield neural network and Boltmann machine in solving the shortest path routing problem in the ATM network environment. Hopfield neural network and Boltzmann machine are two types of neural network which are commonly used for solving optimization problem such as the shortest path routing problem. The objectives of this research are to construct a Hopfield neural network and a Boltzmann machine for solving the shortest path routing problem in the ATM network. Both of these two types of neural network are built based on a chosen example of an A TM network. The Private Network-Node Interface (PNNI) network is a type of A TM network in which the shortest path routing mechanism can be used. Simulation of the shortest path computation for an A TM network is done for both the Hoptield neural network and the Boltzmann machine.

Initial

snms

Additional Information

Dissertation (M.A) -- Faculty of Computer Science & Information Technology, Universiti Malaya, 2001.

Recommended Citation

Lee, Chee Weng, "Hopfield model for shortest path computation and routing in ATM network" (2001). Student Works (2000-2009). 550.
https://knova.um.edu.my/student_works_2000s/550

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.