Investigation on the Classification Accuracy of Harmonic Signal detection by Artificial Neural Network

Harmonic signals are produced by many natural sources as well as man-made sources. The detection of the harmonic signal can reveal a lot about the physical system. Various Fourier harmonic analysis based numerical methods are commonly used to detect such signals. However, recently there has been considerable interest in other non-Fourier-based methods as well, to determine the harmonics. In the present work we have studied the application of artificial neural network based machine learning in frequency identification of sinusoidal signals. We considered training sets comprising harmonic signals with randomised phase or randomised amplitude or combination of the both. Based on such training sets the trained network is then applied to detect the frequency of unknown harmonics. Here we performed an investigation on the relative advantages of the network trained using sets of harmonic signals with different features.