Cam-phasing Optimization Using Artificial Neural Networks as Surrogate Models-Fuel Consumption and NOx Emissions

2006 ◽  
Author(s):  
Bin Wu ◽  
Robert G. Prucka ◽  
Zoran Filipi ◽  
Denise M. Kramer ◽  
Gregory L. Ohl
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuel Consumption ◽  
Surrogate Models ◽  
Nox Emissions ◽  
Artificial Neural

scholarly journals ESTIMATION OF FUEL CONSUMPTION IN AGRICULTURAL MECHANIZED OPERATIONS USING ARTIFICIAL NEURAL NETWORKS

2017 ◽  
Vol 37 (1) ◽  
pp. 136-147 ◽  
Author(s):  
Pedro H. M. Borges ◽  
Zaíra M. S. H. Mendoza ◽  
João C. S. Maia ◽  
Aloísio Bianchini ◽  
Haroldo C. Fernándes
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuel Consumption ◽  
Artificial Neural

Shallow and Deep Artificial Neural Networks for Structural Reliability Analysis

2020 ◽  
Author(s):  
Wellison J. S. Gomes
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reliability Analysis ◽  
Structural Reliability ◽  
Deep Neural Networks ◽  
Surrogate Models ◽  
The Other ◽  
Computational Costs ◽  
Structural Reliability Analysis ◽  
Artificial Neural

Abstract Surrogate models are efficient tools which have been successfully applied in structural reliability analysis, as an attempt to keep the computational costs acceptable. Among the surrogate models available in the literature, Artificial Neural Networks (ANNs) have been attracting research interest for many years. However, the ANNs used in structural reliability analysis are usually the shallow ones, based on an architecture consisting of neurons organized in three layers, the so-called input, hidden and output layers. On the other hand, with the advent of deep learning, ANNs with one input, one output, and several hidden layers, known as deep neural networks, have been increasingly applied in engineering and other areas. Considering that many recent publications have shown advantages of deep over shallow ANNs, the present paper aims at comparing these types of neural networks in the context of structural reliability. By applying shallow and deep ANNs in the solution of four benchmark structural reliability problems from the literature, employing Monte Carlo simulation and adaptive experimental designs, it is shown that, although good results are obtained for both types of ANNs, deep ANNs usually outperform the shallow ones.

scholarly journals State-based load profile generation for modeling energetic flexibility

2019 ◽  
Vol 2 (S1) ◽  
Author(s):  
Kevin Förderer ◽  
Hartmut Schmeck
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Surrogate Models ◽  
Energy Resources ◽  
Distributed Energy Resources ◽  
Demand Side ◽  
Specific Load ◽  
Distributed Energy ◽  
Load Profile ◽  
Artificial Neural

Abstract Communicating the energetic flexibility of distributed energy resources (DERs) is a key requirement for enabling explicit and targeted requests to steer their behavior. The approach presented in this paper allows the generation of load profiles that are likely to be feasible, which means the load profiles can be reproduced by the respective DERs. It also allows to conduct a targeted search for specific load profiles. Aside from load profiles for individual DERs, load profiles for aggregates of multiple DERs can be generated. We evaluate the approach by training and testing artificial neural networks (ANNs) for three configurations of DERs. Even for aggregates of multiple DERs, ratios of feasible load profiles to the total number of generated load profiles of over 99% can be achieved. The trained ANNs act as surrogate models for the represented DERs. Using these models, a demand side manager is able to determine beneficial load profiles. The resulting load profiles can then be used as target schedules which the respective DERs must follow.

scholarly journals Prediction of NOx Emissions for a Range of Engine Hardware Configurations Using Artificial Neural Networks

2021 ◽  
Author(s):  
Nick Papaioannou ◽  
XIAOHANG FANG ◽  
Felix Leach ◽  
Martin Davy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Nox Emissions ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document