J. Lampinen et al., CLASSIFICATION OF POLYNOMIAL-SHAPED MEASUREMENT SIGNALS USING A BACKPROPAGATION NEURAL-NETWORK, IEEE transactions on instrumentation and measurement, 43(6), 1994, pp. 933-936
Smoothly varying signals are frequently encountered in the field of in
strumentation and measurement, and they can be accurately modeled by l
ow-order polynomials. The order identification is difficult when the m
easured noisy signal has frequent order variations in the underlying p
olynomial. In this paper, we introduce a flexible real-time order esti
mator, which is based on a backpropagation neural network.