Samir I. Sayegh
Phys. Dept., Purdue Univ., Fort Wayne, IN 46805
Neural networks have emerged as a viable and effective paradigm for pattern recognition and discrimination. A widely used algorithm, backpropagation, can be sensitive to the representation used for the input patterns to the network. This is particularly true in machine diagnostic. An application is presented to discrimination of good versus bad electric motors. Representations in terms of spectral coefficients, wavelets, and KL coefficients are contrasted.