A fuzzy clustering neural networks for motion equations of synchro-drive robot

Motion equations for synchro-drive robot Nomad 200 are solved by using fuzzy clustering neural networks. The trajectories of the Nomad 200 are assumed to be composed of line segments and curves. The structure of the curves is determined by only two parameters (turn angle and translational velocity in the curve). The curves of the trajectories are found by using artificial neural networks (ANN) and fuzzy C-means clustered (FCM) ANN. In this study a clustering method is used in order to improve the learning and the test performance of the ANN. The FCM algorithm is successfully used in clustering ANN datasets. Thus, the best of training dataset of ANN is achieved and minimum error values are obtained. It is seen that, FCM-ANN models are better than the classic ANN models.