Name
Title | ||
|---|---|---|
Development of a parsimonious GA-NN ensemble model with a case study for Charpy impact energy prediction |
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.advengsoft.2011.03.012 | Advances in Engineering Software |
Keywords | DocType | Volume |
charpy impact energy prediction,neural network candidate model,neural networks,parsimonious neural network ensemble,genetic algorithms,ensemble modelling,case study,qualified neural network candidate,traditional neural network,complex industrial data,model structure optimisation,neural network structure,modelling result,parsimonious ga-nn ensemble model,heat-treated steel,individual neural network model,charpy impact energy,neural network ensemble,ensemble model,neural network model,neural network,fitness function,genetic algorithm | Journal | 42 |
Issue | ISSN | Citations |
7 | Advances in Engineering Software | 5 |
PageRank | References | Authors |
0.49 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y. Y. Yang | 1 | 8 | 1.98 |
| Mahdi Mahfouf | 2 | 235 | 33.17 |
| George Panoutsos | 3 | 57 | 7.59 |