Fast learning by weight estimation in complex valued MLPs

In the paper a strategy in order to decrease the learning time without affecting the learning efficiency for a complex valued Multi Layer Perceptron (CMLP) is proposed. The methodology makes use of auxiliary devices that enable one to predict the connections trend of the principal neural network whose learning phase is not damaged since the auxiliary devices run concurrently with it. A numerical example is reported which shows the suitability of the proposed approach