The article addresses prompt decision-making on coal mine safety in case of accidents by the dispatching personnel. The discussion deals with issues connected with identification of underground accidents using the learning model of fuzzy neural network. The algorithm of fuzzy neural network classification of off-optimum situations (OOS) in coal mines is proposed. Belonging of the learning image of OOS to the existing or a new cluster is determined from the maximum similarity concept. The function of difference between the learning image of OOS and the cluster in the neural network is defined as the Euclidean distance. This function displays two vectors (the learning image of OOS and the cluster) as a real number. Images of each cluster (average values of OOS images in a cluster) are stored in the communication links (weights) of the neural network during the whole process of classification. Application of the classification algorithm in the conditions of partly overlapped classes of accidents is illustrated. If classified clusters are partly overlapped, each image of OOS in the learning family may belong to more than one cluster. Binary matrix serves for recording each image of OOS in a cluster. The algorithm based on a hypothesis that there exists a model for each cluster, which is determined as an average value of models of all images of OOS in this cluster, is used to determine the membership function. Membership of each image of OOS in a cluster is determined from similarity of this image to the cluster model. The similarity can be found as the function of the Euclidean distance between the image and the cluster model. This article uses the triangle membership function in fuzzy set. This function is simple for the mathematical manipulation in equivalent application domain. The competitive learning was applied. In this case, the learning processes is corrected by the weight factor connected with the input and output nodes of neural network. The learning model was applied to identification of existing classes of standard accidents, to modeling these classes and to classification of OOS in coal mines. The quality of this model performance was estimated using a learning set composed of many learning images. The learning model is the base model, and is applicable to dealing with large-scale problems connected with identification of OOS, as well as causes and initiation conditions of accidents. The idea of such identification is formulated.

Kupriyanov V. V. Identification of accident classes in coal mines using the neural network technology. MIAB. Mining Inf. Anal. Bull. 2022;(8):148-157. [In Russ]. DOI: 10.25018/0236_1493_2022_8_0_148.

V.V. Kupriyanov, Dr. Sci. (Eng.), Professor, National University of Science and Technology «MISiS», 119049, Moscow, Russia, e-mail: Kupriyanov.VV@misis.ru, ORCID ID: 000-0003-3793-8361.

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