## 4 Intelligent Height Adjustment Model of Shearer Drum Based on AR-AIGA-FRRBFNN  
To predict the running track of the shearer drum height with high accuracy, this study proposes a shearer drum height prediction model based on AR-AIGA-FRRNFNN. This section mainly introduces the relevant theories of the proposed model, including the theory attribute reduction algorithm based on rough set significance, the theory of fuzzy rough radial basis function neural networks, and the parameter optimization of the fuzzy rough radial basis function neural network based on an adaptive immune genetic algorithm.  
Significance Based Attribute Reduction Algorithm for Initial Feature Parameters of Coal Mining Machine  
Based on the significance of attributes, the minimum attribute reduction set is directly selected as the shearer drum height decision rule set, and the dependency and significance of each conditional attribute are calculated in turn to provide important parameters for subsequent decision-making. When constructing the shearer drum intelligent height adjustment model, it is necessary to perform attribute reduction before training the model. The process of realizing the shearer drum height decision attribute reduction is as follows:  
Step 1: The state attribute domain \(U\) of the shearer and drum is divided into equivalence classes. In this study, the attribute reduction algorithm based on significance is used to divide the attribute set \(C\) and the drum height decision \(D\), the equivalence class \(ind(C)=\{c_{1},c_{2},\ldots,c_{n}\}\), \(ind(D)=\{D_{1},D_{2},\ldots,D_{n}\}\) is obtained.  
Step 2: The dependence of the drum height decision \(D\) on the condition attribute set \(C\) is calculated.  
\[r(C,D)=\frac{|POS_{C}(D)|}{|U|} \tag{11}\]  
Step 3: The significance of the conditional attribute \(C\) that removes a certain attribute \(c_{i}\) to the decision height \(D\) of the drum is calculated.  
\[r(C-c_{i},D)=\frac{\left|POS_{C-\{c_{i}\}}(D)\right|}{|U|} \tag{12}\]  
Step 4: The significance of the conditional attribute \(c_{i}\) for the decision attribute \(D\) is calculated.  
\[sig(c_{i},C,D)=r(C,D)-r(C-c_{i},D) \tag{13}\]  
Equation (13) represents the degree to which a single factor \(c_{i}\) affects the coal miner drum height decision after removing the conditional attribute \(c_{i}\), i.e., the significance of \(c_{i}\) to the decision attribute \(D\). The kernel \(CORE_{D}(C)\) of the conditional attribute set \(C\) relative to the decision attribute \(D\) is calculated, such that \(B=CORE_{D}(C)\).  
Step 5: The influence of the condition \(c_{i}\) on the classification ability of \(B\) on the decision attribute \(D\) is calculated.  
\[sig(c_{i},B,D)=r(B,D)-r(B-c_{i},D) \tag{14}\]  
If \(sig(c_{i},B,D)=0\), this means that removing the conditional attribute \(c_{i}\) does not affect the classification ability of the conditional attribute set \(B\) to the decision attribute \(D\), that is, the conditional attribute \(c_{i}\) is a redundant attribute. The larger the value of \(sig(c_{i},B,D)\), the more important the conditional attribute \(c_{i}\) is, and the greater the influence of \(c_{i}\) on the classification effect of \(B\) on \(D\). The specific process of attribute reduction is shown in Figure 2.  
Figure 2: Attribute reduction flow chart based on attribute significance.