System identification using selforganizing feature maps

A method for identification of mechanical systems is reported. The identification of mechanical systems is often done by neural networks used as black boxes in order to produce an inverse system model for control. Contrary to this approach, we intend to identify the mechanical structure and parameters, which allows the use of conventional control theory. The basis of the identification system is a self-organizing feature map (SOFM) representing the systems to be identified. The systems are described by their response to test signals, which are used for feature extraction. The extracted features are analyzed with SOFMs to explore the feature space. The map is well suited for this kind of interpretation. As an application example, the identification of a two mass system is presented.