## 1 Introduction
However, these methods still cannot fully describe the uncertainty of the coal seam change, which inevitably leads to the redundancy of the characteristic parameters, affecting the drum tuning accuracy. In the latest research on the problem of shearer drum height adjustment, [PERSON] et al. proposed the adaptive fuzzy reasoning Petri net model to adapt to the complex nonlinear problem of the shearer drum cutting trajectory [16]. [PERSON] et al. built the shearer adaptive cutting control model through the multi-objective grey wolf optimizer algorithm [17]. [PERSON] and [PERSON]'s research further analyzed the difficult problem of drum cutting, but they did not consider the redundancy and correlation between the data attributes of multiple input sensors, which reduced the prediction accuracy of the model. Considering the redundancy and correlation between the data attributes of the multi-input sensors, it is necessary to reduce the working characteristic parameters of the shearer to remove the redundant attributes. For attribute reduction, researchers have carried out much research. Among this, the attribute reduction algorithm based on rough set theory has great advantages. It does not need prior knowledge and can objectively evaluate uncertain factors [18; 19]. [PERSON] et al. used the rough set conditional information entropy method to measure the collapse risk of mountain tunnels [20]. [PERSON] et al. used the attribute significance method to objectively weight the various condition attributes that affect the operation to cold air [21]. Therefore, we designed an attribute reduction algorithm based on the significance of rough set theory to reduce the attributes of shearer operating parameters. There is uncertainty in coal seam changes, and there is a complex nonlinear relationship between the cutting track of the shearer drum and the operating characteristic parameters. Artificial neural networks and artificial intelligence algorithms have strong nonlinear fitting and generalization ability [22; 23]. [PERSON] et al. established an accurate estimation model of the impact of blasting operations on the environment by combining genetic programming algorithm (GP) and gene expression programming algorithm (GEP) [24]. [PERSON] et al. built a coal seam permeability prediction model based on multiple linear regression and a GEP algorithm, and effectively predicted the coal seam permeability [25]. The radial basis function neural network is widely used in the field of intelligent control because of its excellent nonlinear approximation [26]. However, when the RBF neural network processes uncertain information, the generalization performance of the trained model is degraded due to the lack of a mechanism to measure the uncertain features [27]. To solve this problem, researchers combined fuzzy theory with a radial basis function neural network, and proposed the fuzzy radial basis function neural network. [PERSON] et al. introduced the Takagi-Sugeno (T-S) fuzzy rule inference system into a radial basis function neural network, and thus constructed the T-S fuzzy radial basis function neural network [28]. [PERSON] et al. defined its basis function to be scalar, and combined this with T-S fuzzy rules to model unsteady aerodynamics, thereby proposing a fuzzy scalar radial basis function neural network [29]. The fuzzy RBF neural network can avoid the shortcomings of slow convergence speed and poor generalization ability of traditional neural network models. However, the fuzzy Radial Basis Function neural network constructed in combination with T-S fuzzy inference system makes the network structure unusually large due to the input of multi-dimensional variables, thus reducing the convergence speed and affecting the model accuracy [30]. Therefore, it is necessary to improve the fuzzy radial basis function neural network to guide the intelligent drum height adjustment in the fully mechanized mining face and realize high-precision coal mining under complex and changeable geological conditions.