HPC strength prediction using Bayesian neural networks

• Autorzy: Słoński M.
• Konferencja: Neural Networks and Soft Computing/International Symposium (30.06-02.07.2005 ; Cracow, Poland)
• Języki publikacji: EN

Abstrakty

EN

The objective of this paper is to investigate the efficiency of nonlinear Bayesian regression for modelling and predicting strength properties of high-performance concrete (HPC). A multilayer perceptron neural network (MLP) model is used. Two statistical approaches to learning and prediction for MLP based on the likelihood function maximization and Bayesian inference are applied and compared. Results of experimental data sets show that Bayesian approach for MLP offers some advantages over classical one.

Słowa kluczowe

EN

Bayesian inference; regression; high-performance concrete; neural network

PL

sieci neuronowe; regresja; wnioskowanie Bayes'a

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Computer Assisted Mechanics and Engineering Sciences
• Rocznik: 2007
• Tom: Vol. 14, No. 2
• Strony: 345--352
• Opis fizyczny: Bibliogr. 15 poz., tab., wykr.

Twórcy

autor

Słoński M.

• Cracow University of Technology, Institute for Computational Civil Engineering, ul. Warszawska 24, 31 -1 55 Kraków, Poland

Bibliografia

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