In this paper two recursive algorithms, based on MOESP type subspace identification, are presented in two versions. The main idea was to show that we can represent subspace identification methods as sequences of least squares problems and implement them through sequences of modified Householder algorithms. Therefore, it is possible to develop iterative algorithms with most of the advantages of ...

this paper presents a method for identification of linear system physical parameters (structural mass, damping and stiffness matrices) using the inverse solution of equation of motion in the frequency domain, by focus on the reducing the illconditioning effect. the method utilizes the measured responses from the forced vibration test of structure in order to identify the system properties and d...

In this paper, estimation and identification theories will be examined with the goal of determining some new methods of adding robustness. The focus will be upon uncertain estimation problems, namely ones in which the uncertainty multiplies the quantities to be estimated. Mathematically the problem can be stated as, for system matrices and data matrices that lie in the sets (A + δA) and (b + δb...

pH control is a challenging problem due to its highly nonlinear nature. In this paper the performances of two different adaptive global linearizing controllers (GLC) are compared. Least squares technique has been used for identifying the titration curve. The first controller is a standard GLC based on material balances of each species. For implementation of this controller a nonlinear state...

genotype×environment interactions (geis) can affect breeding programs because they often complicate the evaluation and selection of superior genotypes. this drawback can be reduced by gaining insights into gei processes and genotype adaptation. the objectives of this research were to evaluate: (1) the yield stability of promising wheat lines across locations and (2) the relationship among the t...

In this paper we investigate the consistency of parameter estimates obtained from least squares identification with a quadratic parameter constraint. For generality, we consider infinite impulse response systems with colored input and output noise. In the case of finite data, we show that there always exists a possibly indefinite quadratic constraint depending on the noise realization that yiel...

This paper considers the practical implementation of a new maximum likelihood robot identification method, developed by Olsen and Petersen. In particular, the practical issue concerning the estimation of the joint velocities and accelerations from joint angle measurements, and its consequence on the parameter estimation and accuracy, is considered. Simulation and experimental results on a KUKA ...

An algorithm for recursive Frisch scheme system identification of linear single-input single-output errors-in-variables systems is developed. For the update of the estimated model parameters, a recursive bias-compensating least squares algorithm, which is based on the wellknown recursive least squares technique, is considered. The estimate of the output measurement noise variance is determined ...

We introduce a recursive adaptive group lasso algorithm for real-time penalized least squares prediction that produces a time sequence of optimal sparse predictor coefficient vectors. At each time index the proposed algorithm computes an exact update of the optimal `1,∞-penalized recursive least squares (RLS) predictor. Each update minimizes a convex but nondifferentiable function optimization ...