Nonlinear synaptic Neural Network for Maximum Flow problems

In advance of network communication society by the Internet, the way how to send data fast with a little loss has become an important transportation problem. A generalized maximum flow algorithm provides the best solution to the transportation problem of determining which route is appropriated to exchange data. Therefore, the importance of the maximum flow algorithm continues to grow. In this paper, we propose a Maximum-Flow Neural Network (MF-NN) in which branch nonlinearity has a saturation characteristic and by which the maximum flow problem can be solved with analog high-speed parallel processing. Moreover, the stability of proposed network is discussed. The proposed neural network for the maximum flow problem can be achieved by using a nonlinear resistive circuit where each connection weight between nodal neurons has a sigmoidal function. The parallel hardware of the MF-NN will be easily implemented.