Tai-an School, Shandong University of Science & Technology, Tai-an, Shandong, China.
Department of Mechanical and Electronic Engineering, Shandong University of Science & Technology, Qingdao, Shandong, China.
PLoS One. 2020 Sep 4;15(9):e0238138. doi: 10.1371/journal.pone.0238138. eCollection 2020.
Coal mining professionals in coal mining have recognized that the assessment of top coal release rate can not only improve the recovery rate of top coal, but also improve the quality of coal. But the process was often performed using a manual-based operation mode, which intensifies workload and difficulty, and is at risk of human errors. The study designs a assessment system to give the caving output ratio in top coal caving as accurately as possible based on the parameters adaptive Takagi-Sugeno (T-S) fuzzy system and the Levenberg-Marquardt (LM) algorithm. The main goal of the adaptive parameters based on LM algorithm is to construct its damping factor in the light of lowering of the objective function which is as taken as the index of termination iteration. The performance of the system is evaluated by Pearson correlation coefficient, Coefficient of Determination and relative error where the results of the Takagi-Sugeno method and the parameters adaptive Takagi-Sugeno method are compared to make the evaluation more robust and comprehensive.
采煤专业人员已经认识到，对顶煤释放率的评估不仅可以提高顶煤的回收率，还可以提高煤炭质量。但这一过程通常采用基于人工的操作模式，这不仅增加了工作量和难度，而且还存在人为错误的风险。本研究设计了一个评估系统，该系统基于参数自适应 Takagi-Sugeno（T-S）模糊系统和 Levenberg-Marquardt（LM）算法，尽可能准确地给出顶煤放顶煤的冒落系数。基于 LM 算法的自适应参数的主要目标是根据目标函数的降低来构建其阻尼因子，目标函数被用作终止迭代的索引。通过皮尔逊相关系数、确定系数和相对误差来评估系统的性能，将 Takagi-Sugeno 方法和参数自适应 Takagi-Sugeno 方法的结果进行比较，使评估更加稳健和全面。
