Πλοήγηση ανά Θέμα "Forecasting"
Αποτελέσματα 41-60 από 125
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Fatigue monitoring and remaining lifetime prognosis using operational vibration measurements
(2019)A framework is presented for real-time monitoring of fatigue damage accumulation and prognosis of the remaining lifetime at hotspot locations of new or existing structures by combining output-only vibration measurements ... -
Fatigue reliability predictions in vibrating structures under uncertainty
(2008)This work addresses the problem of predicting the reliability due to fatigue of MDOF structures subjected to uncertain random loading. Uncertainties in loading characteristics as well as in structural and degradation models ... -
Finite element model validation and predictions using dynamic reduction techniques
(2011)Finite element (FE) model updating and validation techniques are formulated as single and multi-objective optimization problems. A multi-objective optimization framework results in multiple Pareto optimal models that are ... -
Fire resistance prediction of slim-floor asymmetric steel beams using single hidden layer ANN models that employ multiple activation functions
(2022)In this paper a mathematical model for the prediction of the fire resistance of slim-floor steel beams based on an Artificial Neural Network modeling procedure is presented. The artificial neural network models are trained ... -
Forecasting Methods to Support the Decision Framework of Prosumers in Deregulated Markets
(2021)In the present paper, a profit maximization problem for a prosumer is formulated and solved. The prosumer selects the electricity procurement approach among the pool market and forward contracts. Instead of treating the ... -
Forecasting of day-ahead natural gas consumption demand in Greece using adaptive neuro-fuzzy inference system
(2020)(1) Background: Forecasting of energy consumption demand is a crucial task linked directly with the economy of every country all over the world. Accurate natural gas consumption forecasting allows policy makers to formulate ... -
Forecasting urban expansion based on night lights
(2016)Forecasting urban expansion models are a very powerful tool in the hands of urban planners in order to anticipate and mitigate future urbanization pressures. In this paper, a linear regression forecasting urban expansion ... -
A framework for formulating and implementing non-associative plasticity models for shell buckling computations
(2022)In modelling the behavior of thick-walled metal shells under compressive loads, the use of J2 flow theory can lead to unrealistic buckling estimates, while alternative ‘corner’ models, despite offering good predictions, ... -
Fuzzy cognitive maps and multi-step gradient methods for prediction: Applications to electricity consumption and stock exchange returns
(2015)The paper focuses on the application of fuzzy cognitive map (FCM) with multi-step learning algorithms based on gradient method and Markov model of gradient for prediction tasks. Two datasets were selected for the implementation ... -
A Generalised Approach on Kerf Geometry Prediction during CO2 Laser cut of PMMA Thin Plates using Neural Networks
(2021)This study presents an application of feedforward and backpropagation neural network (FFBP-NN) for predicting the kerf characteristics, i.e. the kerf width in three different distances from the surface (upper, middle and ... -
Geophone networks and environmental studies: Application to landslides
(2016)Seismic surveys are a non-invasive method for exploring the geological structure of the Earth. Their purpose is to identify the composition of the subsoil. This information can be used to indicate the presence of potential ... -
Hidden neural networks for transmembrane protein topology prediction
(2021)Hidden Markov Models (HMMs) are amongst the most successful methods for predicting protein features in biological sequence analysis. However, there are biological problems where the Markovian assumption is not sufficient ... -
Hierarchical Bayesian calibration and response prediction of a 10-story building model
(2019)This paper presents Hierarchical Bayesian model updating of a 10-story building model based on the identified modal parameters. The identified modal parameters are numerically simulated using a frame model (exact model) ... -
Hierarchical Bayesian Model Updating for Nonlinear Structures Using Response Time Histories
(2022)This paper presents a novel hierarchical Bayesian modeling (HBM) framework for the model updating and response predictions of dynamic systems with material nonlinearity using multiple data sets consisting of measured ... -
Hierarchical Bayesian modeling framework for model updating and robust predictions in structural dynamics using modal features
(2022)The hierarchical Bayesian modeling (HBM) framework has recently been developed to tackle the uncertainty quantification and propagation in structural dynamics inverse problems. This new framework characterizes the ensemble ... -
Hierarchical Bayesian Uncertainty Quantification for a Model of the Red Blood Cell
(2021)Simulations of blood flows in microfluidic devices and physiological systems are gaining importance in complementing experimental and clinical studies. The predictive capabilities of these simulations hinge on the parameters ... -
An Hour-Ahead Photovoltaic Power Forecasting Based on LSTM Model
(2021)The extensive integration of the large-scale Photovoltaic (PV) plants into the power grid requires the development of new forecasting methods, for the prediction of the PV output with high accuracy. Despite the statistical ... -
Hybrid model for water demand prediction based on fuzzy cognitive maps and artificial neural networks
(2016)In this study, we propose a new hybrid approach for time series prediction based on the efficient capabilities of fuzzy cognitive maps (FCMs) with structure optimization algorithms and artificial neural networks (ANNs). ... -
Identification of most important features based on a fuzzy ensemble technique: Evaluation on joint space narrowing progression in knee osteoarthritis patients
(2021)Objective: Feature selection (FS) is a crucial and at the same time challenging processing step that aims to reduce the dimensionality of complex classification or regression problems. Various techniques have been proposed ... -
Implementing fuzzy cognitive maps with neural networks for natural gas prediction
(2018)The goal of this research study is to test the hardiness of a novel hybrid computational intelligence model in day-ahead natural gas demand prediction. The proposed model combines an evolutionary learned FCM method with a ...