A Performance Comparison of Different Back Propagation Neural Networks in Inverse Kinematics

Error Back Propagation, a class of neural networks, is proposed to solve the inverse kinematics problem in robotic manipulator. In this approach a network has been trained to learn a desired set of joint angles positions from a given set of end effectors positions. This paper demonstrates some methods of Back Propagation neural network which can be used to solve inverse kinematics. Next the performance of these methods has been compared for inverse kinematics problems. The used Error Back Propagation techniques are the Standard, Momentum and Delta Bar- Delta.

Keywords:

Neural networks , Robotic , Inverse kinematics , Back propagation