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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.

With respect to classification problems, the Minimum enclosing ball (MEB) method was recently studied by some scholars as a new support vector machine. As a nascent technology, however, MEB reports poor adaptability for different types of samples, especially multi-class samples. In this paper, we propose a new multi-class classification method based on MEB. This method is derived from each class sample center and radius with the Gaussian kernel width factor parameter σ, which is labelled as σ-MEB. σ is a variable parameter according to the different sample characteristics. When this parameter is considered, the multi-class classifier is easy to adapt and is robust in diverse datasets. The quadratic programming problem was transformed into its dual form with Lagrange multipliers using this method. Finally, we applied sequential minimal optimization method and Karush—Kuhn—Tucker conditions to accelerate the training process. Numerical experiment results indicate that for given different types of samples, the proposed method is more accurate than the methods with which it is compared. Moreover, the proposed method reports values in the upper quantile with respect to adaptation performance.

Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score) feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB) algorithm plus Support vector machine (SVM) is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition.