## 4 Simulation and Analysis
### Clustering Analysis  
The parameters of the proposed FCM-FGOA were set as follows: the weighted index \(m=2\), the threshold value of the iterative process \(\xi=10^{-5}\), the maximal iteration number \(N=100\), the random fly distance range \(L=[-1,\,1]\), the number of fruit flies \(P=20\), the genetic proportion \(P_{g}=0.3\), the crossover probability \(P_{c}=0.8\) and the mutation probability \(P_{m}=0.01\). The preset recognition accuracy on the testing samples was 90%. The clustering process obtained the convergent fitness value at different preset category number \(c\) and selected the maximum as the result. In this paper, the value range of \(c\) was [1; 8] and the iteration process at different category numbers was shown in Figure 8. It could be seen from the figure that the maximal fitness value appeared when \(c\) was 4. Moreover, the sound samples in practice could be divided as S1, S2, S3 and S4, which was very consistent with the simulation results. In order to validate the accuracy of clustering center obtained from the proposed method, the remaining 100 test samples were applied. The membership value of each sample was calculated according to Equation (3) and the classification result was shown in Figure 9. Five misjudgments occurred in the 100 test samples, so the accuracy of the improved was 95%. It could be seen from the figure that two sample points in S1 were misclassified into S2. One sample in S2 was \begin{table}
\begin{tabular}{c c} \hline
**Number** & **Feature Vector** \\ \hline \multirow{2}{*}{\(1\)} & [0.767079, 0.01970, 0.000509, 0.002197, 0.002056, 0.002177, 0.000075, 0.001561, \\  & 0.000001, 0.000006, 0.000021, 0.000020, 0.001224, 0.001112, 0.000045, 0.000417] \\ \hline \multirow{2}{*}{\(2\)} & [0.771856, 0.176547, 0.013883, 0.174835, 0.002439, 0.00517, 0.003403, 0.008946, \\  & 0.000030, 0.000150, 0.000295, 0.000244, 0.001405, 0.001727, 0.000374, 0.000991] \\ \hline \multirow{2}{*}{\(3\)} & [0.744010, 0.118100, 0.067296, 0.063892, 0.031492, 0.002058, 0.055797, 0.008220, \\  & 0.00003, 0.000004, 0.000282, 0.000024, 0.000880, 0.000462, 0.000360, 0.000263] \\ \hline \multirow{2}{*}{\(4\)} & [0.804032, 0.006559, 0.000681, 0.002085, 0.000015, 0.000053, 0.000241, 0.000120, \\  & 0.000001, 0.000006, 0.000011, 0.000009, 0.000011, 0.000015, 0.000011, 0.000013] \\ \hline \multirow{2}{*}{\(399\)} & [0.734019, 0.097592, 0.073854, 0.101107, 0.020851, 0.003079, 0.0601384, 0.009932, \\  & 0.000002, 0.000006, 0.000730, 0.000009, 0.002013, 0.000099, 0.000670, 0.000155] \\ \hline \multirow{2}{*}{\(400\)} & [0.699824, 0.012047, 0.000935, 0.002409, 0.000031, 0.000019, 0.000310, 0.000119, \\  & 0.000003, 0.000011, 0.000017, 0.000004, 0.000009, 0.000037, 0.000007, 0.000022] \\ \hline \end{tabular}
\end{table} 
Table 2: Feature vectors of the 300 samples. Figure 7: Wavelet packet node coefficients. assigned into S1 and another into S3. The sound series in S3 was confused for S2 once and samples in S4 were classified accurately. The reason lies in the fact that the acoustic signal of cutting objects with similar hardness, such as S1 with S2 and S2 with S3, had small differences. Samples especially from the boundary had larger probability to be misjudged. In comparison, those with obvious distinction, such as S4 with other cutting patterns, could be recognized accurately. Finally, ten groups of simulations with different genetic proportion \(P_{g}\) were conducted to get the change rule of the fitness value and the convergence time with the proportion value, and the simulation results were listed in Table 3. The category number \(c\) was 4, the range of \(P_{g}\) was \([0,1]\) and the other parameters kept invariant. It could be seen from the table that the convergent fitness was less than 0.643496 when \(P_{g}\) was less than 0.3, and the value was a consistent 0.643496 when fruit flies conducting genetic operation increased. The computation time when the fitness reached the maximal fitness increased with \(P_{g}\) in general.