Sojoodi, Mahdi, Soleymani, Farzad (2012). System identification by a generalized interior point algorithm and nonlinear optimization methods considering ARMA model. , 12(2), 36-41.
Mahdi Sojoodi; Farzad Soleymani. "System identification by a generalized interior point algorithm and nonlinear optimization methods considering ARMA model". , 12, 2, 2012, 36-41.
Sojoodi, Mahdi, Soleymani, Farzad (2012). 'System identification by a generalized interior point algorithm and nonlinear optimization methods considering ARMA model', , 12(2), pp. 36-41.
Sojoodi, Mahdi, Soleymani, Farzad System identification by a generalized interior point algorithm and nonlinear optimization methods considering ARMA model. , 2012; 12(2): 36-41.

System identification by a generalized interior point algorithm and nonlinear optimization methods considering ARMA model

The Modares Journal of Electrical Engineering
Article 5, Volume 12, Issue 2, 2012, Pages 36-41    PDF (88.05 K)
Authors
Mahdi Sojoodi* 1; Farzad Soleymani2
1Advanced Control Systems Laboratory, School of Electrical and Computer Engineering, Tarbiat Modares University, Tehran,
2Advanced Control Systems Laboratory, School of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
Abstract
In this paper, we describe our implementation of an interior point algorithm for large scale systems. First we identify system with small and medium methods convex optimization, then we use interior point method for identification. Finally we offer an interior point method that uses nonlinear cost function and see that we achieve a good trade-off between error and CPU time. Actually, in this paper, we are looking for a method that can identify large scale systems with low model order, error and CPU time of solution of simulation. Previous articles didn’t check the order of the computed model, and the relationship between the error and CPU time. We assume that the model of our simulation is ARMA. We are going to identify a large scale system and compute the error and CPU time and compare the relationships. Examined data in this paper is related to cutaneous potential recordings of a pregnant woman. These data are pendulous and have a large standard deviation; therefore, it can’t be fitted with ordinary curve fittings, so we use the smoothing spline for computing the order of the model. Finally, we checked the influence of the number of data on error and CPU time and order of model
Keywords
Interior point algorithm; Convex optimization; Large scale ststen; system identification
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