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Information × Registration Number 0213U002795, 0111U001230 , R & D reports Title The pilot neural network system for instant synfhesis of optimal control of melting operational modes in electric arc furnace popup.stage_title Head Lozynsky O. Y., Registration Date 07-02-2013 Organization Lviv Polytechnic National University popup.description2 Expediency of multicriterion optimal control strategies implementation for nonlinear nonstationary objects on the basis of fuzzy sets theory is substantiated. A system for electric arc furnace arcs length control based on fuzzy regulator has been implemented and method of regulator synthesis has been worked out. Obtained results showed improvement in dynamic regulation of arcs lengths during the use of fuzzy correction. The structure of three-contour fast-acting electromechanical system for positioning and dynamic stabilization of coordinates based on pulse-width converter and implementing fuzzy correction of control signal is proposed. The theoretical basis of fuzzy regulators based multicriterion optimal control systems synthesis and analysis have been developed. Additive functional model for optimal control utilizing fuzzy controller for changing weights of partial criteria has been worked through. The influence of membership functions parameters on dynamic indices has been studied. The structure of arc lengths control system based on neural controllers NARMA-L2 Controller and NN Predictive Controller have been developed. Neural controllers synthesis for electric arc furnace arc lengths control system have been done and numeric simulations on created mathematical and digital models have been carried out. Obtained results showed improved accuracy of electric arc furnace electric mode coordinates dynamic stabilization while using neural controllers. A method of functional based multicriterion control signal operative synthesis is proposed, as an additive linear combination of partial criteria with variable in time weights through the use of fuzzy controllers. An approach for the formation of fuzzy controller belonging function form, which provides not only the optimum transition to the desired level of performance, but also the desired behavior of the system under the influence of external disturbances. The method of neural network based dynamograms recognition for rod deep oil well pumping installation is proposed. The structure of the optimal control of rod deep pumping plant based on neural network is proposed. A mathematical and numerical model of rod deep pumping installation has been developed and its dynamics has been explored. Product Description popup.authors Головач І.Р. Демків Л.І. Лозинський А.О. Лозинський О.Ю. Маляр А.В. Марущак Я.Ю. Михайлович Т.І. Мороз В.І. Паранчук Р.Я. Паранчук Я.С. Парначук З.Л. popup.nrat_date 2020-04-02 Close