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Документ Diploidy-based Genetic Algorithm in Nonstationary Environment(Харьковский государственный политехнический университет, 1995) Klepikov, V. B.; Lysenko, L. I.; Mahotilo, K. V.; Sergeev, S. A.Документ Evolutionary Synthesis of Dynamical Object Emulator Based on RBF Neural Network(Nagoya University, 1996) Sergeev, S. A.; Mahotilo, K. V.The 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.Документ Amplitude-Frequency Characteristic of a Neural Control Based DC Drive(1996) Sergeev, S. A.; Klepikov, V. B.; Mahotilo, K. V.The paper interprets characteristics of a neural-control-based DC servodrive in terms of the classical theory of automatic control. It also touches on the problem of choosing training patterns to synthesize a nonlinear PID-controller with a desired amplitude-frequency characteristic and analyses the efficiency of using for this purpose input signals in form of a step function and a harmonic one. Synthesis of the neurocontroller has been performed within the framework of a three-layer perceptron. To train it, a genetic algorithm has been developed.Документ Neural Technologies in Electrical Drive Control(1995) Klepikov, V. B.; Mahotilo, K. V.; Sergeev, S. A.; Voronovsky, G. K.