Model Identification for WCET Analysis

2009 ◽  
Author(s):  
Björn Lisper ◽  
Marcelo Santos
Keyword(s):  
Model Identification ◽  
Wcet Analysis

Iterative Synthesis of Control with Selective Model Identification

2004 ◽  
Vol 36 (5) ◽  
pp. 1-9
Author(s):  
Vyacheslav F. Gubarev
Keyword(s):  
Model Identification ◽  
Iterative Synthesis

PI(D) tuning with contextual model identification

2009 ◽  
Author(s):  
Alberto Leva ◽  
Sara Negro ◽  
Alessandro Vittorio Papadopoulos
Keyword(s):  
Model Identification ◽  
Contextual Model

Improvement of methods for sparse model identification of pulsed geophysical signals

2019 ◽  
Author(s):  
Alexander Tristanov ◽  
Olga Lukovenkova ◽  
Yuri Marapulets ◽  
Alina Kim
Keyword(s):  
Model Identification ◽  
Sparse Model

Model identification approach for aircraft failure detection on take-off maneuver using a motion-based simulator

2017 ◽  
Author(s):  
Aline Dahleni Kraemer ◽  
Diego Hernandez Arjoni ◽  
Emilia Villani
Keyword(s):  
Model Identification ◽  
Failure Detection ◽  
Identification Approach

Probabilistic Assessment of Structural Condition Incorporating Uncertainty of Measured Data through Multi-model Identification

2015 ◽  
Vol 105 (19) ◽  
pp. 1-8
Author(s):  
Hyun-Joong Kim ◽  
Hyun-Moo Koh ◽  
Junho Song ◽  
Wonsuk Park
Keyword(s):  
Model Identification ◽  
Structural Condition ◽  
Measured Data ◽  
Probabilistic Assessment

Model Fungsi Transfer Input Ganda untuk Pemodelan Jakarta Islamic Index

2019 ◽  
Vol 7 (3) ◽  
Author(s):  
Nur Laela Fitriani ◽  
Pika Silvianti ◽  
Rahma Anisa
Keyword(s):  
Exchange Rate ◽  
Transfer Function ◽  
Multivariate Time Series ◽  
Model Identification ◽  
Transfer Function Model ◽  
Function Model ◽  
Jones Index ◽  
Multiple Input ◽  
Identification Stage ◽  
Stage Estimation

Transfer function model with multiple input is a multivariate time series forecasting model that combines several characteristics of ARIMA models by utilizing some regression analysis properties. This model is used to determine the effect of output series towards input series so that the model can be used to analyze the factors that affect the Jakarta Islamic Index (JII). The USD exchange rate against rupiah and Dow Jones Index (DJI) were used as input series. The transfer function model was constructed through several stages: model identification stage, estimation of transfer function model, and model diagnostic test. Based on the transfer function model, the JII was influenced by JII at the period of one and two days before. JII was also affected by the USD exchange rate against rupiah at the same period and at one and two days before. In addition, the JII was influenced by DJI at the same period and also at period of one until five days ago. The Mean Absolute Prencentage Error (MAPE) value of forecasting result was 0.70% and the correlation between actual and forecast data was 0.77. This shows that the model was well performed for forecasting JII.

NARMAX Model Identification Using Multi-Objective Optimization Differential Evolution

2018 ◽  
Vol 10 (7) ◽  
Author(s):  
Mohd Zakimi Zakaria ◽  
◽  
Zakwan Mansor ◽  
Azuwir Mohd Nor ◽  
Mohd Sazli Saad ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Model Identification ◽  
Multi Objective Optimization ◽  
Multi Objective

Designing Experiments for Model Identification of the Nitrification Process

1991 ◽  
Vol 24 (6) ◽  
pp. 9-16 ◽  
Author(s):  
P. J. Ossenbruggen ◽  
H. Spanjers ◽  
H. Aspegren ◽  
A. Klapwijk
Keyword(s):  
Mechanistic Model ◽  
Nonlinear Differential Equations ◽  
Model Identification ◽  
Reaction Rates ◽  
Variable Coefficients ◽  
Model Specification ◽  
First Order ◽  
Interactive Behavior ◽  
Batch Tests ◽  
Nitrification Process

A series of batch tests were performed to study the competition for oxygen by Nitrosomonas and Nitrobacter in the nitrification of ammonia in activated sludge. Oxygen uptake rate (OUR) and dynamic (compartment) models describing the process are proposed and tested. The OUR model is described by a Monod relationship and the biogradation process by a set of first order nonlinear differential equations with variable coefficients. The results show a mechanistic model and ten reaction rates are sufficient to capture the interactive behavior of the nitrification process. Methods for model specification, calibrating, and testing the model and the design of additional experiments are described.

scholarly journals An extended instrument variable approach for nonparametric LPV model identification

2018 ◽  
Vol 51 (26) ◽  
pp. 81-86 ◽  
Author(s):  
Marcelo M.L. Lima ◽  
Rodrigo A. Romano ◽  
Paulo Lopes dos Santos ◽  
Felipe Pait
Keyword(s):  
Model Identification ◽  
Variable Approach ◽  
Instrument Variable

scholarly journals Methodologies for the WCET Analysis of Parallel Applications on Many-Core Architectures

2015 ◽  
Author(s):  
Vincent Nelis ◽  
Patrick Meumeu Yomsi ◽  
Luis Miguel Pinho
Keyword(s):  
Parallel Applications ◽  
Wcet Analysis ◽  
Many Core
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