## 4 Simulation Results
### Case (2): Holographic Optical Tweezers Manipulate Control for Multiple Cells
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Data Type** & **Main Controller** & **Cell 1** & **Cell 2** & **Cell 3** & **Cell 4** \\ \hline \multirow{6}{*}{RMSE of each cell in the \(X\)-axis direction} & PI & \(4.7\times 10^{-6}\) & \(5.1\times 10^{-6}\) & \(4.7\times 10^{-6}\) & \(5.0\times 10^{-6}\) \\  & BP & \(2.2\times 10^{-6}\) & \(2.7\times 10^{-6}\) & \(2.2\times 10^{-6}\) & \(2.7\times 10^{-6}\) \\  & RBF & \(7.0\times 10^{-8}\) & \(5.0\times 10^{-8}\) & \(6.4\times 10^{-10}\) & \(7.0\times 10^{-8}\) \\  & FCMNN & \(8.4\times 10^{-15}\) & \(1.4\times 10^{-19}\) & \(2.4\times 10^{-10}\) & \(2.1\times 10^{-12}\) \\  & This work & \(2.1\times 10^{-20}\) & \(1.8\times 10^{-20}\) & \(2.8\times 10^{-15}\) & \(2.1\times 10^{-15}\) \\ \hline \multirow{6}{*}{RMSE of each cell in the \(Y\)-axis direction} & PI & \(2.2\times 10^{-6}\) & \(2.1\times 10^{-6}\) & \(1.9\times 10^{-6}\) & \(2.0\times 10^{-6}\) \\  & BP & \(2.1\times 10^{-7}\) & \(2.1\times 10^{-7}\) & \(1.2\times 10^{-7}\) & \(1.2\times 10^{-7}\) \\ \cline{1-1}  & RBF & \(3.3\times 10^{-8}\) & \(5.0\times 10^{-8}\) & \(5.1\times 10^{-8}\) & \(7.7\times 10^{-8}\) \\ \cline{1-1}  & FCMNN & \(2.7\times 10^{-15}\) & \(1.9\times 10^{-19}\) & \(7.5\times 10^{-11}\) & \(7.1\times 10^{-12}\) \\ \cline{1-1}  & This work & \(3.1\times 10^{-20}\) & \(9.0\times 10^{-21}\) & \(1.6\times 10^{-15}\) & \(2.1\times 10^{-15}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RMSE of each controller. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Data Type** & **Main Controller** & **Cell 1** & **Cell 2** & **Cell 3** & **Cell 4** \\ \hline \multirow{6}{*}{MAE of each cell in the \(X\)-axis direction} & PI & \(2.6\times 10^{-5}\) & \(3.1\times 10^{-5}\) & \(2.5\times 10^{-5}\) & \(2.9\times 10^{-5}\) \\  & BP & \(6.5\times 10^{-6}\) & \(7.2\times 10^{-6}\) & \(6.5\times 10^{-6}\) & \(7.2\times 10^{-6}\) \\  & RBF & \(1.5\times 10^{-8}\) & \(2.1\times 10^{-8}\) & \(8.9\times 10^{-9}\) & \(2.8\times 10^{-8}\) \\  & FCMNN & \(3.4\times 10^{-16}\) & \(3.8\times 10^{-20}\) & \(9.9\times 10^{-12}\) & \(4.8\times 10^{-14}\) \\  & This work & \(5.4\times 10^{-21}\) & \(4.6\times 10^{-21}\) & \(1.1\times 10^{-16}\) & \(8.5\times 10^{-18}\) \\ \hline \multirow{6}{*}{MAE of each cell in the \(Y\)-axis direction} & PI & \(5.7\times 10^{-6}\) & \(5.2\times 10^{-6}\) & \(4.0\times 10^{-6}\) & \(4.0\times 10^{-6}\) \\  & BP & \(2.1\times 10^{-7}\) & \(2.1\times 10^{-7}\) & \(4.1\times 10^{-8}\) & \(4.0\times 10^{-8}\) \\ \cline{1-1}  & RBF & \(7.9\times 10^{-9}\) & \(1.7\times 10^{-8}\) & \(1.7\times 10^{-8}\) & \(2.5\times 10^{-8}\) \\ \cline{1-1}  & FCMNN & \(1.1\times 10^{-16}\) & \(7.6\times 10^{-21}\) & \(3.0\times 10^{-12}\) & \(2.9\times 10^{-13}\) \\ \cline{1-1}  & This work & \(5.6\times 10^{-23}\) & \(3.0\times 10^{-23}\) & \(6.4\times 10^{-17}\) & \(8.4\times 10^{-17}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MAE of each controller. When the error begins to gradually converge and the optical trap moves slowly, the number of layers and blocks of the NSOFCMNN can also be reduced accordingly. The number of layers is adjusted from 6 to 4, and the number of blocks is reduced from four to three and then to two blocks, thus reducing the learning time of the network. Since \(T_{n}\), \(T_{m}\) and \(T_{k}\) are slightly different between single-cell manipulation and multi-cell manipulation experiments, the final layer and block changes are also different. The settings of the self-organizing structure adjustment parameter in the two experiments were compared, and the differences in the structural changes are presented in Figures 8 and 14. It can be found that the smaller the value of \(T_{n}\) is, the easier it is for the NSOFCMNN to increase the number of layers and blocks, thereby improving the learning ability of the network. When the values of \(T_{m}\) and \(T_{k}\) are larger, NSOFCMNN will be easier to reduce the number of layers and blocks, thus improving the learning efficiency of the network.