Robust Design of Artificial Neural Network Methodology to Solve the Inverse Kinematics of a Manipulator of 6 DOF

Abstract

In this work the artificial neural networks robust design methodology was used to find the optimal parameters in backpropagation artificial neural network architecture applied to the solution of the inverse kinematics of a six degrees of freedom robotic manipulator. A systematic and experimental strategy is proposed according to the requirements of the problem. Among the various parameters that affect the performance of a backpropagation neural network, four design variables were selected since they are the variables that can be controlled by the user: the number of neurons in the first hidden layer, the number of neurons in the second hidden layer, the momentum, and the learning rate. The results show that the robust design methodology can be used to find a better configuration with high performance and generalization capability, significantly reducing the time spent determining the optimal architecture of the neural network compared to the trial and error approach, which requires long periods of time.