Affiliations: [a] College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| [b] College of Electronic Information Engineering, Shandong University of Science and Technology, Qingdao, China
Correspondence:
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Corresponding author. Zhang Xiaojun, College of Electronic Information Engineering, Shandong University of Science and Technology, Qingdao, China. E-mail: [email protected].
Abstract: In view of identification of strong mine quake and rock burst, the mine geology, structural mechanics, mining production data and acoustic emission monitoring process data are infused to build two fuzzy process neural network models based on fuzzy set theory. The model integrates fuzzy logic inference mechanism with process neural network process signal analysis and learning capacity. It presents domain knowledge based on fuzzy set and membership function, and adaptively establishes computational logic and fuzzy decision rules based on process signal distribution features, which can effectively infuse multi-source information and prior knowledge, demonstrating good ability to comprehensively analyze various quantitative and qualitative mixed information and identify microquake features, as well as small sample modeling capacity. It has good adaptability for predictive analysis of strong mine quake and rock burst with uncertainty.
Keywords: Coal rock mass impact, fuzzy, neural network
