Group Method Of Data Handling Neural Network Research Articles

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

Diagnosis of Sugar Factory Processes Using GMDH Neural Networks

This paper deals with GMDH (Group Method of Data Handling) neural networks and their application in Fault Detection and Isolation (FDI) systems. Such networks can be considered as feedforward networks with a ‘growing’ structure during the training process. To apply the GMDH networks in the FDI for real technological processes we propose some extentions of the GMDH theory to multi-output networks and dynamic modelling. The proposed networks and their extensions have been implemented in an industrial model-based diagnostic system for some units of the Lublin sugar factory in Poland. Finally, simulation results show the effectiveness of the proposed neural networks in FDI systems.