On Signal Analysis with ANN and PSO Techniques

The work presents some results of signal analysis, periodic and aperiodic behavior, with artificial intelligence techniques. Two paradigms are considered, based on artificial neural networks (ANN) and – the second one – based on particle swarm optimization (PSO). The first approach promotes linear neural networks architecture, ADALINE for single signals and MADALINE for multiple signals case. The second approach promotes the swarm intelligence, used here for the minimization of objective function based on mean squared error (MSE). For periodic signals, the trigonometric Fourier series is considered. For aperiodic signals, the discrete Fourier transform is promoted. Both techniques could be applied for a more general problem of model parameter estimation. The computer-based experiments are made with various signal-to-noise ratios, to study the effects on the converge rate on the estimation process. At least for the considered signals, the ANN based solution provides better results. The framework could be extended to the analysis problem based on other base functions, e.g., wavelets.