A.V. Gulay, V.M. Zaitsev Intelligent models of energy-converting systems: construction and application
An intelligent model of the energy-converting system is proposed, which is implemented by means of provision of the energy conversion scheme in the form of an net graph along with subsequent building of behavioral rules of crisp and fuzzy logic. Links of the PART-OF type are established between the considered system and graph nodes, what pertinently turns the net graph to a semantically oriented model of energy-converting system functioning. This simulation method ensures formation of a certain quantity of structurally oriented frames for allocated system functional blocks, where each of them conforms to an individual top of the net graph. Distinctive features of control procedures and diagnostics of the considered energy-converting system are achieved due to the identification of frames and slot, included to them. A bypass circuit is also presented for tops of the built net graph, as well as a sequence of actions for system dynamical control at different simulation levels. In order to provide possible account of experimental and expert main production rules of crisp and fuzzy logic have been worked out, which reflect combination and variation of a group of parameters, as well as facts of system features degradation in the course of time.
energy-converting system; intelligent model; production rules; net graph; fuzzy sets.
1. Blinov, A.V. 2005. Intellektual`nye sistemy diagnostiki i prognozirovaniya [Intelligent systems of diagnostics and forecasting]. Datchiki i sistemy [Sensors & Systems]. 9:65–70.
2. Karibskiy, V.V., P.P. Pakhomenko, E.S. Sogomonyan, and V.F. Khalchev. 1976. Osnovy tekhnicheskoy diagnostiki [Fundamentals of technical diagnostics]. Moscow: Energy Publs. 464 p.
3. Gulyaev, V.A. 1983. Tekhnicheskaya diagnostika upravlyayushchikh sistem [Technical diagnostics of control systems]. Kyiv: Science thinking. 207 p.
4. Buravlyov A.I., B.I. Dotsenko, and I.E. Kazakov. 1995. Upravlenie tekhnicheskim sostoyaniem dinamicheskikh sistem [The control of technical condition of a dynamic systems]. Moscow: Machine engineering. 240 p.
5. Sen`chenkov V.I. 2004. Matematicheskaya model` vhoda i vyhoda protsessov sistemy kak ob`ekta kontrolya tekhnicheskogo sostoyaniya [Mathematical model of the input and output of the system processes as an control object of technical condition]. Izvestiya VUZov. Priborostroenie [Proceedings of high schools. Instrument engineering] 47(5):44–49.
6. Sen`chenkov V.I. 2005. Formirovanie mnozhestva kontroliruemykh priznakov sistemy na osnove metricheskoy teorii i funktsional`nogo analiza [The set of system controlled features formation on the metric theory and functional analysis base]. Izvestiya VUZov. Priborostroenie [Proceedings of high schools. Instrument engineering] 48(7):13–19.
7. Gulay, A.V., and V.M. Zaytsev. 2015. Informatsionnyy klasternyy podhod v proektirovanii intellektual`nykh mekhatronnykh sistem [Information cluster approach in designing of intelligent mechatronic systems]. Elektronika-info [Electronics-info] 7:42–46.
8. Gulay, A.V., and V.M. Zaytsev. 2016. Analiz informatsionnykh faktorov v proektirovanii intellektual`nykh mekhatronnykh sistem [Analysis of information factors for designing intelligent mechatronic system]. Nauka i tekhnika [Science & Technique]. 4:335–344.
9. Gulay, A.V., and V.M. Zaytsev. 2016. Kontseptual`nye shemy predmetnykh oblastey v tekhnologii postroeniya intellektual` nykh sistem [Analysis of information factors in designing of intelligent mechatronic systems]. Elektronika-info. [Electronics-info] 10:56–61.
10. Zadeh, L.A. 1973. The сoncept of a linguistic variable and its application to approximate reasoning. N.Y.: Elsevier. 165 p.
11. Shtovba, S.D. 2007. Proektirovanie nechyotkikh sistem sredstvami MATLAB [Designing of fuzzy systems by means of MATLAB facilities]. Moscow: Goryachaya liniya Publs. 288 p.