Confidence estimation of GMDH neural networks and its application in fault detection systems

Abstract

This article deals with the problem of determination of the model uncertainty during the system identification via application of the self-organising group method of data handling (GMDH) neural network. In particular, the contribution of the neural network structure errors and the parameter estimates inaccuracy to the model uncertainty were presented. Knowing these sources and applying the Outer Bounding Ellipsoid (OBE) algorithm it was possible to calculate the uncertainty of the parameters and the model output. The mathematical description of the model uncertainty enabled designing the robust fault detection system, whose effectiveness was verified by the DAMADICS benchmark.

Keywords:

• system identification
• model uncertainty
• neural networks
• bounded-error modelling
• non-linear systems
• fault diagnosis
• application