  The stochastic parallel gradient descent (SPGD) algorithm is usually employed
as the control strategy for phase-locking in fiber laser phased array systems.
However, the convergence speed of the SPGD algorithm will slow down as the
number of array elements increases. To improve the control bandwidth, the
convolutional neural network is introduced to quickly calculate the initial
piston aberration in a single step. In addition, the irrationality of the
commonly used Mean Square Error (MSE) evaluation function in existing
convolutional neural networks is analyzed. A new evaluation function NPCD
(Normalized Phase Cosine Distance) is proposed to improve the accuracy of the
neural networks. The results show that the piston aberration residual is 0.005
and the power in the bucket (PIB) is 0.993 after accurate preliminary
compensation, which means that the system directly enters the co-phase state.
We also demonstrate the robustness and scalability by adding additional
disturbance and expanding the scale of the array.
