Bibliography: 1. Lagunova Yu. A., Shestakov V. S. Imitatsionnoe modelirovanie pri raschetenii parametrov konusnykh crushilok [Simulation modeling when calculating parameters of cone crushers]. Ekate-rinburg: UGGA, 1988. pp. 52—53. [In Russ]
2. Beisembayev K. M., Ibraeva N. R. Neural network approach to the development, modeling and management of mining machines. Development of models and control elements of technological machines. 2017. [In Russ]
3. Kalyanov A. E., Lagunova Yu. A. Mathematical modeling of elements of the hydraulic scheme of the cone crusher pressing system. Mining equipment and electromechanics. 2014. no. 2. pp. 39—45. [In Russ]
4. Kalyanov A. E., Lagunova Yu. A., Shestakov V. S., Calculation of parameters of cone crushers for the passage of an unbroken body. Technological equipment for the mining and oil and gas industry: collection of reports of the XIV International Conference “Readings in Memory of V. R. Kubachek”. Yekaterinburg: UGGU Publishing House, 2016. pp. 187—194. [In Russ]
5. Ibraeva N. R., Malybaev N. S., Komissarov A. P., Lagunova Yu.A., Shestakov V. S. Computer program study of the parameters of the working process and the movement of pieces through the crushing chamber of a cone crusher. Certificate of entering information into the state register of rights to objects protected by copyright no. 8055 dated February 10, 2020. Type of copyright object is a work of science. Date of creation of the object 19.10.2019 Republic of Kazakhstan. [In Russ]
6. Narendra K. S., Parthasarathy K. Identification and control of dynamic systems using neural networks. IEEE Trans. on Neur. Net. 1990. vol.1. no. 1. pp. 4—27.
7. Dmitriev V. T., Timukhin S. A., Simisinov D. I., Karyakin A. L. Analysis of energy parameters of mine hoists. Gornyi Zhurnal. 2017. no. 8, pp. 70—72. DOI: 10.17580/ gzh.2017.08.13 [In Russ]
8. Klepikov, V. B., Sergeev S. A., was Mahotella K. V., Hoop I. V. application of the methods of neural networks and genetic algorithms in solving the problems of control of electric drives. Electrical engineering, no. 5, 1999. –P. 2—6. [In Russ]
9. I. G. Conger, I. R. Nizamov neural network regulator-ditch the drive control system. Herald technological University. 2017. Vol. 20, no. 8 [In Russ]
10. Lagunova, Y. A. Bochkov, V. S. Energy Component of Properties of Material Crushability Layer. (2020) Lecture Notes in Mechanical Engineering, pp. 577—584. https:// www.scopus.com DOI: 10.1007/978—3-030—22063—1_61
11. Tijmen Tieleman and Geoffrey Hinton. Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning, 4(2):26—31, 2012
12. Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. NeurIPS Workshop on Machine Learning Systems, 2015.
13. Wang ZH, Tu L, Guo Z, Yang LT, Huang BX. Analysis of user behaviors by mining large network data sets[J]. Future generation computer systems. The international journal of grid computing and escience. 2014,37: 429—437.
14. Chen CY, Chiang JS, Chen KY, Liu TK, Wong CC. An Approach for Fuzzy Modeling based on Self-Organizing Feature Maps Neural Network[J]. Applied mathematics & information sciences. 2014,8(3):1207—1215.