00065 and MAE can reach 0.00987. Actually, the values of MSE and MAE basically keep stable at the times of 280, which can show

good convergence performance of proposed method. After the training phase, a T-S CIN model can be obtained. In order to verify the accuracy of the model, the remaining 50 samples are utilized to test its performance. The prediction order SAR131675 errors and deviation comparison diagrams of the network output and actual output are given as Figure 8. As shown in Figure 8, the MSE and MAE of testing samples are 0.006118 and 0.0346, respectively, showing good generalization performance. Furthermore, the mean relative error and maximum relative error are 1.23% and 5.78%, which satisfies the accuracy requirement. Figure 8 Comparison of network output and actual output. 4.4. Comparison with Other Methods In order to indicate the meliority of T-S CIN integrating IPSO, the T-S CINs based on the basic PSO (bPSO), CPSO, and IPSO are provided to solve the

problem of above example. The training samples and testing samples are the same. The configurations of simulation environment for three algorithms are uniform and the relevant parameters are in common with above example. The compared learning curves with MSE and MAE of T-S CIN models based on bPSO, CPSO, and IPSO can be shown in Figure 9 and some performance criterions are listed in Table 1, where 50_MSE and 50_MAE are the values of MSE and MAE in the stage

of 50 iterations. Furthermore, MRE and MaxRE denote the mean relative error and maximum relative error of the network output and actual output. Figure 9 The compared learning curves with MSE and MAE of T-S CIN based on bPSO, CPSO, and IPSO. Table 1 The compared criterions of T-S CIN based on bPSO, CPSO, and IPSO. Seen from Figure 9 and Table 1, the declining velocity of the error of CPSO and IPSO is faster than that of bPSO during the training phase. The MAE of IPSO-based T-S CIN gets to <0.05 for 30 iterations and the MSE of training phase reaches a stable phase for 300 iterations. However, the training errors of MAE with the bPSO, CPSO-based T-S CIN model are still 0.05026 and 0.1293 for 30 iterations. In the testing phase, the Dacomitinib test sample error of bPSO, CPSO-based T-S CIN is much larger than the same input conditions of proposed method. By analysis, the criterions of CPSO-based T-S CIN are more excellent than these of other methods both in the training stage and in the testing stage, which proves the effectiveness and feasibility of proposed method. In order to verify the superiority of T-S CIN (T-S NN coupling cloud model), the sample data in Figure 6 are used to test the performance of T-S CIN and conventional T-S FNN, and the proposed IPSO is also integrated with the two networks. Thus, four algorithms are developed, marked as T-S FNN, T-S CIN, T-S FNN_IPSO, and T-S CIN_IPSO.