Authors: N.R. Ibrayeva

Title of the article: Analysis of the scope of neural networks in mining machines

Year: 2021, Issue: 6, Pages: 21-25

Branch of knowledge: 05.05.06 Mining machines

Index UDK: 622.271

DOI: 10.26730/1816-4528-2021-6-21-25

Abstract: This article presents an analysis of the neural network in mining machines. Research and analysis of the traditional work of a neural network optimizes it in accordance with the characteristics of data mining.With the development and popularization of technologies, data mining is also constantly evolving. It is widely used in the field of artificial intelligence and information processing. Neural networks have become standard and important tools for data mining in industry. Conclusions are drawn about the use of artificial intelligence in mining machines. The main ways of using and implementing neural networks were also identified. Neural networks will increase the probability of increasing the reliability of recognition, detection and localization of emergency situations. The possibility of using artificial neural networks in relation to solving the problem of diagnostics and forecasting of the KMD is considered. Effective design and modeling of mining machines, information and software components are one of the bases in the database, which further allows you to create a shell that combines automatic design, modeling and monitoring of the current state of the machine with one structurally related component. The cases presented in the article require significant simplification and universalization of database creation, which is due to the need for greater adaptation of systems to work with automated algorithms and is a very important and necessary component. Neural network modeling can be applied on the basis of databases with multidimensional classifications of objects, as well as neural networks forming a structure of hierarchical groups of nodes and subnodes.

Key words: neural networks mining machines cone crusher neural network analysis database

Receiving date: 13.11.2021

Approval date: 03.12.2021

Publication date: 24.12.2021

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