NEURO-FUZZY CONTROL OF CONTINIOUS STEEL STRIP PICKLING
The paper covers the methods and approaches of intelligent process control of pickling cold rolled steel strip with elements of comparator defect identification, based on the use of radial-basis (RBF) networks with Gaussian activation functions (GRB). The authors offer a criterion for assessing the quality of the process of etching the residual defects of the strip at the exit from the installation. The hypersurface of the process parameters’ change of the etching solution and MISO-model stabilization of process parameters in an optimal area for cost criteria are presented. The method of fuzzy color identification of defects on the steel strip by luminance component segmentation and positioning, and the approach to the construction of a fuzzy regulator of pressure in the nozzles of the hydraulic unit prior irrigation strip defects are offered. To study the process and the synthesis of the classifier and controller the authors used the data obtained in the course of the experiment in the production process.
Ilyunin O.O., Gakhov R.P., Shamraev A.A. NEURO-FUZZY CONTROL OF CONTINIOUS STEEL STRIP PICKLING // Research Result. Information technologies
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