Based on the empirical studies, the speed levels can be divided Survivin Pathway as 0~2.0m/min (Class I), 2.0~3.5m/min (Class II), 3.5~4.5m/min (Class III), 4.5~6.0m/min (Class IV), 6.0~7.5m/min (Class V), and 7.5~9.0m/min (Class VI). However, as the information in the database is collected after the workers operate the coal mining equipment, the information

maybe not very ideal and practical. Therefore, a threshold of 0.2 is introduced to express the subjective factors, and the traction speed levels from the database can be processed and described as Figure 5. Figure 5 Redefined levels of traction speed. Taken Class 1 (Class I) as an example, the level of speed 0~2m/min can be redefined as follows: ClassSp1New=−0.4Sp+1,0

Group Co., 400 groups of samples are randomly extracted and rearranged as shown in Figure 6. Figure 6 Sample data of this example. 4.2. Parameters Selection for Proposed Method There are some parameters in IPSO which need to be specified by the user. However, it is unnecessary to tune all these parameters for the sample data because IPSO is not very sensitive to them. Therefore, these parameters are set as the number of particles M(50); the maximum number of allowable iterations T(500); the position and velocity range of particles ([−1, 1]); the initial acceleration coefficients c1 and c2 of IPSO (2.5 and 0.5); the inertia weights wmax and wmin of IPSO (0.9 and 0.4); the termination error Minerr(0.0001); the minimum fitness variance for mutation σmin 2(0.001).

The structure of T-S CIN is determined by the sample data. In this simulation example, the input data of T-S CIN is 6-dimensional and output data is 1-dimensional. Thus, n = 6 and m can be set as 12. Other parameters including expectation Exij, entropy Enij, hyper entropy Heij, and coefficient ωij can be optimized through IPSO. 4.3. Simulation Results The sample data in Figure 6 should be normalized firstly and are randomly split into a training data set containing 350 samples and a testing data set containing the remaining 50 samples, which is only used to verify the Brefeldin_A accuracy and the effectiveness of the trained T-S CIN model. The relevant parameters are given as Section 4.3 described. The proposed method runs 10 times and the mean values are regarded as the final results. The performance criterion of T-S CIN can be measured by the mean squared absolute error (MSE) and the mean absolute error (MAE) between the predicted outcome and the actual outcome. The learning curves with MSE and MAE of T-S CIN model based on IPSO can be shown in Figure 7. Figure 7 The learning curves of T-S CIN model based on IPSO.