研究目的
Definition of the approach to the construction of the neural network algorithm for detection of emergency and pre-emergency situations by multicriterial electro-optical system.
研究成果
The study concludes that neural networks offer significant advantages for forecasting and identifying emergency and pre-emergency situations in coal mines. It proposes a structure for a neural network algorithm to predict these situations and defines threshold limit values for controlled gases. Further research is suggested to develop a neural network for identifying the causes of these situations.
研究不足
The study focuses on the theoretical development of a neural network algorithm and its structure for forecasting situations in coal mines. Practical implementation and testing of the proposed algorithm are not covered.
1：Experimental Design and Method Selection:
The study analyzes two approaches for forecasting situations in coal mines: autoregression and neural networks. It also examines two approaches for recognizing situations based on the dynamics of gas concentration changes and the ratios of several gases' concentrations.
2：Sample Selection and Data Sources:
Data from normative and legal documents, as well as previous research, are used to define threshold limit values for controlled gases in coal mines.
3：List of Experimental Equipment and Materials:
The study proposes the use of MatLab for modeling the neural network.
4：Experimental Procedures and Operational Workflow:
The study involves the analysis of existing approaches, classification of situations in coal mines, determination of threshold limit values, and the proposal of a neural network structure for situation forecasting.
5：Data Analysis Methods:
The study compares the accuracy of different forecasting and recognition approaches, with a focus on the advantages of neural networks.
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