Using artificial neural networks for predicting ship fuel consumption
In marine vessel operations, fuel costs are major operating costs which affect the overall
profitability of the maritime transport industry. The effective enhancement of using ship fuel …
profitability of the maritime transport industry. The effective enhancement of using ship fuel …
Applying artificial neural networks for modelling ship speed and fuel consumption
This paper deals with modelling ship speed and fuel consumption using artificial neural
network (ANN) techniques. These tools allowed us to develop ANN models that can be used …
network (ANN) techniques. These tools allowed us to develop ANN models that can be used …
A new PHO-rmula for improved performance of semi-structured networks
D Rügamer - International Conference on Machine Learning, 2023 - proceedings.mlr.press
Recent advances to combine structured regression models and deep neural networks for
better interpretability, more expressiveness, and statistically valid uncertainty quantification …
better interpretability, more expressiveness, and statistically valid uncertainty quantification …
Landslide displacement interval prediction using lower upper bound estimation method with pre-trained random vector functional link network initialization
Interval prediction is an efficient approach to quantifying the uncertainties associated with
landslide evolution. In this paper, a novel method, termed lower upper bound estimation …
landslide evolution. In this paper, a novel method, termed lower upper bound estimation …
Deep ensembles from a bayesian perspective
L Hoffmann, C Elster - arXiv preprint arXiv:2105.13283, 2021 - arxiv.org
Deep ensembles can be considered as the current state-of-the-art for uncertainty
quantification in deep learning. While the approach was originally proposed as a non …
quantification in deep learning. While the approach was originally proposed as a non …
Neural networks for variational problems in engineering
R López, E Balsa‐Canto… - International Journal for …, 2008 - Wiley Online Library
In this work a conceptual theory of neural networks (NNs) from the perspective of functional
analysis and variational calculus is presented. Within this formulation, the learning problem …
analysis and variational calculus is presented. Within this formulation, the learning problem …
Artificial neural network for predicting values of residuary resistance per unit weight of displacement
Sažetak This paper proposes the usage of an Artificial neural network (ANN) to predict the
values of the residuary resistance per unit weight of displacement from the variables …
values of the residuary resistance per unit weight of displacement from the variables …
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
A major challenge in sample-based inference (SBI) for Bayesian neural networks is the size
and structure of the networks' parameter space. Our work shows that successful SBI is …
and structure of the networks' parameter space. Our work shows that successful SBI is …
A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training
Z Yang, T Zhang, D Zhang - Cognitive neurodynamics, 2016 - Springer
Extreme learning machine (ELM) is a novel and fast learning method to train single layer
feed-forward networks. However due to the demand for larger number of hidden neurons …
feed-forward networks. However due to the demand for larger number of hidden neurons …
Geometric semantic genetic programming with normalized and standardized random programs
Geometric semantic genetic programming (GSGP) represents one of the most promising
developments in the area of evolutionary computation (EC) in the last decade. The results …
developments in the area of evolutionary computation (EC) in the last decade. The results …