摘要：AbstractPiezoeletric materials are used on high-precision and high-dynamics applications, such as for acoustic transmission. This paper covers the challenges of creating a black-box model for a simultaneous acoustic transmission problem, with data acquired in a laboratory setup. The system performance is analyzed for three different models: AutoRegressive Moving Average with eXogenous inputs (ARMAX) model, Nonlinear AutoRegressive with eXogenous inputs (NARX) model with artificial neural network structure, and Nonlinear AutoRegressive Moving Average with eXogenous inputs (NARMAX) models. The best results of each models are compared with respect to precision in free-run simulation. The prediction results show that the most complex NARMAX model had the best results, what encourages further research in creating nonlinear mathematical data-driven abstractions for the piezoacoustic transmission application.
关键词：KeywordsIdentificationcontrol methodsSmart StructuresMechatronicsNonlinear system identificationArtificial Neural NetworksPiezoacoustic Transmission
