Applied Computational Intelligence and Soft Computing

Research Article

Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study

Table 11

Optimal premise and consequent parameters of ZO Sugeno model tuned by GA.
Input variablesOptimal premise parametersOptimal consequent parameters
𝑆 𝐶 𝑆 1 = 0 . 3 0 0 0 𝑢 1 = − 1 . 0 0 0 0
𝜎 𝑆 1 = 0 . 1 3 1 2 𝑢 2 = − 0 . 5 0 0 0
𝐶 𝑆 2 = 0 . 2 8 6 8 𝑢 3 = 0 . 9 9 9 8
𝜎 𝑆 2 = 0 . 4 4 3 8 𝑢 4 = 0 . 1 2 6 1
𝐶 𝑆 3 = 0 . 9 0 0 0 𝑢 5 = 0 . 7 4 9 9
𝜎 𝑆 3 = 0 . 2 2 5 0 𝑢 6 = 0 . 9 9 9 3
𝑢 7 = 1 . 0 0 0 0
𝑢 8 = 1 . 0 0 0 0
𝑢 9 = 1 . 0 0 0 0
𝐹 𝐶 𝐹 1 = 0 . 2 5 0 8 𝑢 1 0 = 0 . 0 0 0 0
𝜎 𝐹 1 = 0 . 1 3 2 2 𝑢 1 1 = 0 . 8 7 3 2
𝐶 𝐹 2 = 0 . 3 5 0 0 𝑢 1 2 = 0 . 0 0 0 0
𝜎 𝐹 2 = 0 . 3 5 0 0 𝑢 1 3 = 0 . 7 5 0 0
𝐶 𝐹 3 = 0 . 7 2 5 0 𝑢 1 4 = − 0 . 3 7 5 0
𝜎 𝐹 3 = 0 . 4 7 5 0 𝑢 1 5 = 0 . 3 7 5 0
𝑢 1 6 = 1 . 0 0 0 0
𝑢 1 7 = 0 . 9 3 7 5
𝑢 1 8 = 0 . 5 3 1 3
𝐷 𝐶 𝐷 1 = 0 . 2 0 0 9 𝑢 1 9 = − 0 . 1 8 7 5
𝜎 𝐷 1 = 0 . 5 7 0 8 𝑢 2 0 = 0 . 6 2 5 0
𝐶 𝐷 2 = 0 . 7 0 5 1 𝑢 2 1 = − 0 . 8 1 3 9
𝜎 𝐷 2 = 0 . 2 8 7 5 𝑢 2 2 = 0 . 3 2 8 1
𝐶 𝐷 3 = 0 . 8 3 7 5 𝑢 2 3 = 0 . 2 5 0 0
𝜎 𝐷 3 = 0 . 1 0 7 8 𝑢 2 4 = 1 . 0 0 0 0
𝑢 2 5 = 0 . 6 5 6 2
𝑢 2 6 = 0 . 5 0 0 0
𝑢 2 7 = 0 . 5 0 0 0