A study of Hopfield neural networks with external noises
Hideki Asai, Kotoko Onodera, Takeshi Kamio, Hiroshi Ninomiya · 2002
This paper describes the influence of the external noise with the autocorrelation on the Hopfield neural network approach to optimization problems. In order to investigate the noise effects, several types of external noises, namely chaotic noises and uniformly random noises, are injected into the network and the improvement of the ability to trace the optimal solution is examined for the travelling salesman problem (TSP). As a result, it is confirmed that the autocorrelation has a large influence on the frequency of transitions among the network states, and the noises generated from the logistic map and the double scroll attractor are useful for tracing solutions.