ДИІГНОСТИКА НЕСПРИЯТЛИВИХ СИТУАЦІЙ В ПРОЦЕСІ ВИРОЩУВАННЯ МОНОКРИСТАЛІВ НАПІВРОВІДНИКІВ
This article presents a fuzzy adaptive model, developed by the authors, to detect adverse situations in the process of semiconductor single-crystals growing. The developed model uses, for rules customization, individual weight coefficients of membership functions for each factor or rule. The weight coefficients values were optimized for each rule through the evolutionary algorithm. In addition, the model estimates the criticality level of a situation. It was gained the knowledge from the experts’ information, and all the parameters were grouped according to the situation class. As a research result the hierarchical knowledge base was created to troubleshoot and make recommendations as for correction of the single-crystal growth running. This database comprises the module responding to two and more critical situations occurring simultaneously.