On Model Identification of Gaussian Reciprocal Processes from the Eigenstructure of Their Covariances

1993 ◽  
pp. 63-71 ◽  
Author(s):  
Carlos F. Borges ◽  
Ruggero Frezza
Keyword(s):  
Model Identification ◽  
Reciprocal Processes

PI(D) tuning with contextual model identification

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

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

EFFECTS OF TREND AND PERIODICITY ON THE CORRELATION DIMENSION AND THE LYAPUNOV EXPONENTS

2008 ◽  
Vol 18 (12) ◽  
pp. 3679-3687 ◽  
Author(s):  
AYDIN A. CECEN ◽  
CAHIT ERKAL
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Lyapunov Exponents ◽  
Correlation Dimension ◽  
Time Series Data ◽  
Logistic Map ◽  
Model Identification ◽  
Series Data ◽  
Largest Lyapunov Exponent ◽  
The Impact

We present a critical remark on the pitfalls of calculating the correlation dimension and the largest Lyapunov exponent from time series data when trend and periodicity exist. We consider a special case where a time series Zi can be expressed as the sum of two subsystems so that Zi = Xi + Yi and at least one of the subsystems is deterministic. We show that if the trend and periodicity are not properly removed, correlation dimension and Lyapunov exponent estimations yield misleading results, which can severely compromise the results of diagnostic tests and model identification. We also establish an analytic relationship between the largest Lyapunov exponents of the subsystems and that of the whole system. In addition, the impact of a periodic parameter perturbation on the Lyapunov exponent for the logistic map and the Lorenz system is discussed.

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