Empirical study on a robotic manipulator concludes that training sets larger than 125 samples yield no further gains in accuracy or efficiency for feedforward neural network inverse kinematics solvers.
D.: A closed loop inverse kinematics solver intended for offline calculation optimized with GA.Robotics, 7(1), 7 (2018)
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How Many Training Samples Are Needed for the Inverse Kinematics Solutions by Artificial Neural Networks
Empirical study on a robotic manipulator concludes that training sets larger than 125 samples yield no further gains in accuracy or efficiency for feedforward neural network inverse kinematics solvers.