A flexible identification strategy for dabnet models

摘要

The focus of this paper is to introduce a flexible identification strategy for a general class of models. We use DABNet models, which are composed of a linear state space system whose states, do- coupled by input, are mapped by a neural network. The linear state space matrices can be represented as loosely coupled first and second order sections. A set of conditions will be analyzed for this kind of model that allows an identification process to be as flexible as possible. By flexible we mean a set of features that would be desirable to find in an identification approach: constraint handling and optimal-input design.