Elman neural networks and time integration for object recognition

We consider a system based on an Elman network for a categorization task. Four objects are investigated by an automa walking around in circles. The shapes are derived from four version of a cross: square, thick cross, critical cross and thin cross. Therefore, the input of the system is represented by the distance-wave relieved by the sensor at each step. We let several parameters vary: starting point and speed of the automa walk, radius of the circle and size of the shape. The system is trained using a back-propagation algorithm. We describe the complete setup of the parameters and noises, which the automa will have to face for the prediction/categorization task