Abstract
In previous reports, an analysis of the basic mathematical methods used to solve the pattern recognition problem was carried out. The inappropriateness of applying the Bayesian classification and cluster analysis to solve the problem of recognizing pre-emergency situations in the process of drilling a well is shown. As a mathematical apparatus for solving the problem of determining the current state of an object of research by a given set of features, a pattern recognition method based on an artificial neural network is selected. In this paper, an analysis is made of existing approaches to improving the quality of education aimed at improving the efficiency of its functioning. The results obtained in this paper will improve the quality of work of the previously developed modified algorithm for training the pre-emergency classifier based on the back propagation method, which differs from the classical one by the procedure for finding the global minimum of the error function, and its software implementation has been implemented. The work is an integral part of previously published developments presented in the materials of articles in 2-nd, 3-rd and 4-th International innovative mining symposiums (2017-2019).
Highlights
The previously proposed general structure of the neural network classifier for preemergency situations has shown the possibility and feasibility of solving the recognition problem for each pre-emergency situation separately, which requires justification of the decomposition of the task of constructing a neural network classifier.In this connection, in this work, a substantiation of the developed structure of the neural network classifier is proposed, consisting of one hidden layer with the number of neurons equal to the number of classifier inputs.The obtained results confirm the previously stated generalized method for recognizing emergency situations in the process of industrial drilling of coal wells. *© The Authors, published by EDP Sciences
Based on the results obtained, it can be concluded that when the value of K is more than three, the recognition accuracy increases insignificantly, while the number of iterations of learning continues to increase in proportion to the value of K
Let us consider the influence of the learning rate of the error back propagation algorithm on the number of iterations of this algorithm and on the number of necessary machine operations, on which the training time directly depends
Summary
The previously proposed general structure of the neural network classifier for preemergency situations has shown the possibility and feasibility of solving the recognition problem for each pre-emergency situation separately, which requires justification of the decomposition of the task of constructing a neural network classifier.In this connection, in this work, a substantiation of the developed structure of the neural network classifier is proposed, consisting of one hidden layer with the number of neurons equal to the number of classifier inputs.The obtained results confirm the previously stated generalized method for recognizing emergency situations in the process of industrial drilling of coal wells. *© The Authors, published by EDP Sciences. The previously proposed general structure of the neural network classifier for preemergency situations has shown the possibility and feasibility of solving the recognition problem for each pre-emergency situation separately, which requires justification of the decomposition of the task of constructing a neural network classifier. In this connection, in this work, a substantiation of the developed structure of the neural network classifier is proposed, consisting of one hidden layer with the number of neurons equal to the number of classifier inputs.
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