Summary
International Symposium on Nonlinear Theory and its Applications
2008
Session Number:B4L-E
Session:
Number:B4L-E2
Maximum Flow Problem to be solved based on Unidirectional Cellular Neural Network
Masatoshi Sato, Hisashi Aomori, Mamoru Tanaka,
pp.-
Publication Date:2008/9/7
Online ISSN:2188-5079
Summary:
In advanced of networked society by the internet, the way how to send data fast with a little loss becomes an important problem. It is a generalization maximum flow algorithm to give the best solution for the network-flow problem that which route is better to excange data. Therefore, the importance of the maximum flow algorithm is growing more and more. In this paper, we propose a novel neural network having a saturation characteristic for maximum flow problem. The proposed neural network can be realized by using nonlinear resistive network having a saturation characteristic of current. Especially, since neural network can independently process the operation in each neuron, it has a very high parallel processing performance. That is, the proposed method can be applied to the large-scale network-flow problem by a large-scale parallel processing.