Robotic modeling and control using a fuzzy neural network

A fuzzy neural network (FNN) is applied to modeling and control of a robot. Comparisons are made between the FNN and standard back propagation neural networks, as well as commercially available neural network software packages for modeling the robot. Observations on the robustness of these networks are presented. A number of experiments demonstrate that the FNN can learn faster and more accurately than the back propagation and commercial neural networks for modeling and control of a real robot.<<ETX>>