Multilayer feedforward networks are universal approximators
Neural Networks
Machine Learning
Finite Elements in Analysis and Design
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence: A Systems Approach
Artificial Intelligence: A Systems Approach
Editorial: Hybrid intelligent algorithms and applications
Information Sciences: an International Journal
Advances in Engineering Software
Fusion of soft computing and hard computing: computational structures and characteristic features
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Cooperative coevolution of artificial neural network ensembles for pattern classification
IEEE Transactions on Evolutionary Computation
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The assessment of failure force in bolted lap joints is a critical parameter in the design of steel structures. This kind of bolted joint shows a highly nonlinear behaviour so traditional analytical models are not very reliable. By contrast, other classical technique like finite element analysis provides a powerful tool to solve nonlinearities but usually with a high computational cost. In this article, we propose a data-driven approach based on multilayer-perceptron network ensemble model for failure force prediction, using a data set generated via finite element simulations of different bolted lap joints. Numeric ensemble methods combine multiple predictors to obtain a single output through average. Moreover, a procedure based on genetic algorithms is used to optimize the ensemble parameters. Results show greater generalization capacity than single prediction model. The resulting ensemble includes the advantages of finite element method whereas reduces the complexity and requires less computation.