Solving ability of Hopfield Neural Network with scale-rule noise for QAP
Yoshifumi Tada, Yoko Uwate, Yoshifumi Nishio · 2008
One of the applications of neural network is solving combinatorial optimization problems. In our past study, the solving ability of the Hopfield Neural Network with noise for quadratic assignment problem is investigated. However, even if we injected the noise to the network, the optimal solution cannot occasionally be found. In this study, we propose the method adding scale-rule noise to the Hopfield Neural Network to achieve better performance. By computer simulations solving quadratic assignment problem, we evaluate the performance of the method.