Adaptive linear learning for on-line harmonic identification: An overview with study cases

This work reviews Adaline-based techniques for estimating Fourier series. The Adaline, with its linear structure and learning, fits a Fourier series by expressing any periodic signal as a sum of harmonic terms. The learning with elementary harmonic inputs enforces the weights to converge to the amplitudes. The Adaline therefore individually identifies the amplitudes of the harmonic terms present in the measured signal in real-time. Relevant study cases are provided. Performances are evaluated and show that harmonic terms of the signals are efficiently estimated.