Classification of polynomial-shaped measurement signals using a backpropagation neural network

Smoothly varying signals are frequently encountered in the field of instrumentation and measurement, and they can be accurately modeled by low-order polynomials. The order identification is difficult when the measured noisy signal has frequent order variations in the underlying polynomial. In this paper, we introduce a flexible real-time order estimator, which is based on a backpropagation neural network.<
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