基于高斯过程回归和深度强化学习的水下扑翼推进性能寻优方法

杨映荷, 魏汉迪, 范迪夏, 李昂

Optimization Method of Underwater Flapping Foil Propulsion Performance Based on Gaussian Process Regression and Deep Reinforcement Learning

YANG Yinghe, WEI Handi, FAN Dixia, LI Ang

表3 联合运动参数与扑翼推进速度的GPR算法参数

Tab.3 GPR model parameters of motion parameters and flapping foil propulsion speed

核函数 Ls Nl On M R P Matern 3/2 0.000 688 71 523 16 2.128 3.108 0.864 Matern 5/2 0.000 562 11 281 12 12.597 16.011 0.640 ARD Matern 3/2 [2.385 7.976 6.998 4.158] 39 948 1 2.128 3.108 0.864 ARD Matern 5/2 [5.088 1.123 8.345 5.086] 32 307 18 12.597 16.011 0.640 Squared exponential 0.008 38 19 108 7 1.274 1.516 0.956 Absolute exponential 0.007 72 32 667 100 1.006 1.832 0.957