## 3 Improved Grey Wolf Optimizer Algorithm to Optimize Support Vector Machine
### Improved GWO Algorithm
Otherwise, it should remain unchanged. 5. Update \(a^{\prime}\), \(A\), and \(C\) according to Formulas (11) and (18). 6. When the number of iterations reaches the set maximum value, terminate the iteration, and the SVM model's optimal \([c,g]\) parameter combination will be output. Otherwise, return to step 3 and continue the iteration. The pseudocode of the IGWO optimization process is shown in Algorithm 1. Figure 1: Comparison of the convergence trends of the convergence factors. ``` Initialize \(n\), \(Iter_{max}\), and other parameters,  Initialize the location of parent population, mutant population and offspring population, and calculate the corresponding individual target fitness value,  Identify \(\alpha\), \(\beta\), \(\delta\) wolves in the parent population, [\(\sim\)sort_index] = sort (parent_wolf); parent_a_Position = parent_Position (sort_index (1), ); parent_a_wolf = parent_wolf (sort_index (1)); parent_b_Position = parent_Position (sort_index (2), ); parent_b_Position = parent_Position (sort_index (3), ); Fitness = zeros (1, \(Iter_{max}\)); Fitness (1) = parent_a_wolf; for \(t=1\):\(Iter_{max}\)  Calculate the value of the nonlinear convergence factor according to formula (24); for \(p=1\):\(n\)  Update the parent individual location according to Formulas (15)-(21);  Calculate the fitness value of the parent individual;  end  Generate the mutant population according to Formula (22)  Generate the offspring population, and perform the crossover operation according to Formula (23);  Calculate the fitness value of new offspring; for \(p=1\):\(n\)  if offspring_wolf (\(p\)) < parent_wolf (\(p\))  parent_wolf (\(p\)) = offspring_wolf (\(p\))  Fitnessbest = parent_wolf (\(p\))



## 5 Experiment and Analysis

### Cutting Sound Signal Acquisition

The experimental data of this paper came from the shearer simulating cutting experimental system made by the research group, as shown in Figure 3.

The system was based on the MG2X160/710 WD thin shearer as the prototype. It took the cutting drum as the research object. Meanwhile, a similarity coefficient of \(\frac{1}{4}\) was applied to develop the system [49]. According to the existing data, the compressive strength of artificial coal samples is about 2.5 times that of natural coal samples. This indicates that a simulated coal sample with a compressive strength of 1 MPa is equivalent to a natural coal sample with a compressive strength of 2.5 MPa. The experiment determined the compressive strength calculation formula between the simulated and natural coal samples. \[\sigma_{m}\text{=}\sigma\cdot\mu\cdot\varphi \tag{26}\]

Figure 3: Shearer simulating cutting experimental system. Figure 2: Flow chart of the cutting pattern recognition model. where \(\sigma_{m}\) is the compressive strength of the simulated coal wall, \(\sigma\) represents the compressive strength of the natural coal sample, \(\mu\) refers to the similarity coefficient, and \(\varphi\) is the strength coefficient. According to Formula (26), the compressive strength relationship between the simulated coal sample and the natural coal sample in this experimental system was 1/10. This indicates that the simulated coal sample with a compressive strength of 1 MPa was similar to a natural coal sample with a compressive strength of 10 MPa. Thus, the experiment involved the pouring of three simulated coal walls with a length \(\times\) width \(\times\) height of 700 mm \(\times\) 200 mm \(\times\) 500 mm according to the proportioning scheme. These strengths were 1 MPa, 2 MPa, and 3 MPa, and the corresponding natural coal sample strengths were 10 MPa, 20 MPa, and 30 MPa. Meanwhile, the shearer cut the simulated coal walls of 1 MPa, 2 MPa, and 3 MPa, which were marked as cutting mode 1, cutting mode 2, and cutting mode 3, respectively. When cutting, the cutting drum forward speed was set to 0.5 m/min. It inputted a frequency of 30 Hz through the inverter so that the cutting motor speed was 858 r/min.