An Aproach of Design and Training of Artificial Neural Networks By Applying Stochastic Search Method

Although vast research works have been paid (throughout some 20 years back) regarding formal synthesis of an ANN it is somehow still open issue. This paper does not consider the mentioned formal synthesis aspects but intends to introduce an original engineering approach offering some advantages whenever training and designing of artificial neurel networks is under consideration. In that sense the author uses powerful combined method of stochastic direct search with well created specific algorithm, in some cases having advantages over numerous known methods wich are based on the application of the dgradient. The said algorithm incorporates universal aproximator, simulation during designing & optimisation process i.e. training. The offered approach is applicable for wide range of artificial neural networks types icluding recurrent ones in real time. The presented numerical examples illustrate applicability of the offered advanced approach