Development of a parsimonious GA-NN ensemble model with a case study for Charpy impact energy prediction

  • Authors:

  • Yong Yao Yang;Mahdi Mahfouf;George Panoutsos

  • Affiliations:

  • Institute for Microstructural and Mechanical Process Engineering: The University of Sheffield (IMMPETUS), Department of Automatic Control and Systems Engineering, The University of Sheffield, Mapp ...;Institute for Microstructural and Mechanical Process Engineering: The University of Sheffield (IMMPETUS), Department of Automatic Control and Systems Engineering, The University of Sheffield, Mapp ...;Institute for Microstructural and Mechanical Process Engineering: The University of Sheffield (IMMPETUS), Department of Automatic Control and Systems Engineering, The University of Sheffield, Mapp ...

  • Venue:
  • Advances in Engineering Software
  • Year:
  • 2011

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Abstract

A parsimonious genetic algorithm guided neural network ensemble modelling strategy is presented. Each neural network candidate model to participate in the ensemble model is structurally selected using a genetic algorithm. This provides an effective route to improve the performance of the individual neural network models as compared to more traditional neural network modelling approaches, whereby the neural network structure is selected through some trial-and-error methods or heuristics. The parsimonious neural network ensemble modelling strategy developed in this paper is highly efficient and requires very little extra computation for developing the ensemble model, thus overcoming one of the major known obstacles for developing an ensemble model. The key techniques behind the implementation of the ensemble model, include the formulation of the fitness function, the generation of the qualified neural network candidate models, as well as the specific definitions of the assemble strategies. A case study is presented which exploits a complex industrial data set relating to the Charpy impact energy for heat-treated steels, which was provided by Tata Steel Europe. Modelling results show a significant performance improvement over the previously developed models for the same data set.