A second-order neural network is designed to be invariant to changes in rotation. Rotation invariance is achieved through a special arrangement of the network structure. The training set only requires one view of each target object. We describe the weight sharing strategy and present a compact disk recognition neural network illustrating its usefulness. The simulation results show that the proposed neural network can distinguish between the target compact disks independent of the transformation in rotation.
