In the framework of nonlinear system identification, LS-SVM is a powerful technique for black-box modelling. This research is aimed to the development of methodologies and algorithms for the application of LS-SVM within the context of data-driven (prediction error) system identification, dealing with issues of large-scale implementation, model selection, model structure, etc. In particular, the research is oriented to develop and exploit model structures that may contain linear components, in such a way that the complexity of the final model can be improved substantially.
Researcher(s): Marcelo Espinoza