Structured learning in feedforward neural networks with application to robot trajectory control
Antonios V. Sideris, K. Orita · 1991
The authors propose a method for structured learning in feedforward neural networks (FFNNs) which results in improved generalization properties and significantly faster training times for the task of controlling the motion of a two-link robotic manipulator over a desired trajectory. They use a control system configuration consisting of a conventional feedback controller and a neural network configured as a feedforward controller. The authors compare the performance of the structured neural network (SNN) to a standard FFNN and also to the cerebellar model articulation controller (CMAC). Through computer simulations, they establish that SNN gives excellent results, outperforming both FFNN and CMAC.>