Sergeev, S. A.Mahotilo, K. V.2017-07-072017-07-071996Sergeev S. A. Evolutionary Synthesis of Dynamical Object Emulator Based on RBF Neural Network / S. A. Sergeev, K. V. Mahotilo // Procs of 1st Workshop on Soft Computing WSC-1,1996, August 19-30, On the Internet, Served by Nagoya University, pp. 31-36.https://repository.kpi.kharkov.ua/handle/KhPI-Press/30585The combination of Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs) has already resulted in researchers advancing in quite a few real world applications but it is in control that this alliance yields much appreciable benefit. The paper reports a Radial Basis Function (RBF) network training technique which joins together global strategy of GAs and a local adjusting procedure typical for RBF networks. While activation function window centres and widths are processed via a "slow" numeric GA, output-layer neurone synaptic weights are defined by a "fast" analytical method. The technique allows to minimize not only the network hidden-layer size but also the pattern set required for training the adequate dynamical object neuroemulator.engenetic algorithmsneural networksRBFmodellingArticlehttps://orcid.org/0000-0001-7081-071X