Fault Detection with Dynamic GMDH Neural Networks: Application to the DAMADICS Benchmark Problem

This paper presents a relatively new identification method based on artificial neural networks, which can be used for both static and dynamic systems. In particular, a Group Method of Data Handling (GMDH) neural network with dynamic neurons is considered. The final part of the paper shows how to use the proposed approach to tackle fault detection of the DAMADICS benchmark