Abstract
Methods of adapting automated control systems have been studied in order to determine the advantages of intellectual methods. It has been shown that the application of a self-learning neurofuzzy network enables one to implement adaptive setting parameters of a regulator and provide the given quality criteria and robotic systems’ ability to function in base and regulation modes in a relatively quick, simple manner. The need for many varying parameters in the system setting of the controlled object has been proved.
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Original Russian Text © V.S. Mikhailenko, R.Yu. Kharchenko, 2014, published in Avtomatika i Vychislitel’naya Tekhnika, 2014, No. 6, pp. 33–45.
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Mikhailenko, V.S., Kharchenko, R.Y. Analysis of traditional and neuro-fuzzy adaptive system of controlling the primary steam temperature in the direct flow steam generators in thermal power stations. Aut. Control Comp. Sci. 48, 334–344 (2014). https://doi.org/10.3103/S0146411614060066
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DOI: https://doi.org/10.3103/S0146411614060066