Neural network recognition of objects based on impact dynamics

A system is presented which can classify unknown objects by the waveform produced upon their impact with a known object. The output of an accelerometer mounted on the known object is read into a unit that computes the waveform's discrete Fourier transform (DFT), which is then fed into a two-layer neural network recognition module. The specific application described observes a collision between two objects, one of which is a wooden platform while the other is made out of a different material. After being shown sample waveforms produced by collisions with three types of objects, the system can then classify new collisions with the objects within 6 ms after the impact. Both the DFT unit and the classification network are implemented with Intel's 80170NX Electrically Trainable Analog Neural Network (ETANN). >