  Artificial neural networks (ANN) have proven to be effective in dealing with
the identification nonlinear models for highly complex systems. To still make
use of the prior information available from baseline models derived from, e.g.,
first-principles (FP), methods have been developed that integrate the prior
knowledge into the identification algorithm for the ANN in a variety of
methods. These methods have shown better estimation speeds and/or accuracy on
unseen data. Among these methods are model augmentation structures. A variety
of these structures have been considered in literature, there is however no
unifying theory to these. In this paper, we propose a flexible
linear-fractional-representation (LFR) based model augmentation structure. This
model structure is able to represent many common model augmentation structures,
thus unifying them under the proposed model structure. Furthermore, we
introduce an identification algorithm capable of estimating the proposed model
augmentation structure. The performance and generalization capabilities of the
identification algorithm and the augmentation structure is demonstrated on a
hardening mass-spring-damper simulation example.
