Bayesian neural networks and Gaussian processes in identification of concrete properties / Marek Słoński // Computer Assisted Mechanics and Engineering Sciences. – 2011, Vol. 18, No. 4, Spec. Iss. ... więcej

Bayesian neural networks and Gaussian processes in identification of concrete properties / Marek Słoński // Computer Assisted Mechanics and Engineering Sciences. – 2011, Vol. 18, No. 4, Spec. Iss. II, s. 291-302 : tab., wykr. – Bibliogr. 18 poz., Streszcz. ang. – Mat. konf.: 57th Annual Conference on Scientific Problems of Civil Engineering, Krynica, Poland, 18-22.09.2011. – ISSN 1232-308X

This paper gives a concise overview of concrete properties prediction using advanced nonlinear regression approach and Bayesian inference. Feed-forward layered neural network (FLNN) with Markov chain Monte ... więcej

This paper gives a concise overview of concrete properties prediction using advanced nonlinear regression approach and Bayesian inference. Feed-forward layered neural network (FLNN) with Markov chain Monte Carlo stochastic sampling and Gaussian process (GP) with maximum likelihood hyperparameters estimation are introduced and compared. An empirical assessment of these two models using two benchmark problems are presented. Results on these benchmark datasets show that Bayesian neural networks and Gaussian processes have rather similar prediction accuracy and are superior in comparison to linear regression model.