Superimposed exponential signals play an important role in Statistical Signal Processing and Time series analysis. In this note, the asymptotic behavior of the least squares estimators of the parameters are obtained in presence of stationary noise for the undamped exponential model. It is well known that this model does not satisfy the sufficient conditions of Jennrich (1969), Wu (1981) or Kund...

 Abstract: Determination of the diffusion coefficient on the base of solution of a linear inverse problem of the parameter estimation using the Least-square method is presented in this research. For this propose a set of temperature measurements at a single sensor location inside the heat conducting body was considered. The corresponding direct problem was then solved by the application of the ...

Determining the diffusion coefficient based on the solution of the linear inverse problem of the parameter estimation by using the Least-square method is presented. A set of temperature measurements at a single sensor location inside the heat conducting body is required. The corresponding direct problem will be solved by an application of the heat fundamental solution.

In this paper, we deal with the problem of continuous-time time-varying parameter estimation in stochastic systems, under three different kinds of stochastic perturbations: additive and multiplicative white noise, and coloured noise. The proposed algorithm is based on the least squares method with forgetting factor. Some numerical examples illustrate the effectiveness of the proposed algorithm....

In this paper, we deal with the problem of continuous-time timevarying parameter estimation in stochastic systems, under 3 different kinds of stochastic perturbations: additive and multiplicative white noise, and colored noise. The proposed algorithm is based on the Least Squares Method with forgetting factor. Some numerical examples illustrate the effectiveness of the proposed algorithm. An an...

The recursive least squares (RLS)-type whitening algorithm is shown to be a useful solution for improving the convergence rate of the least mean square (LMS)-type whitening algorithm, but the RLStype algorithm with a constant forgetting factor requires a tradeoff between the convergence speed and steady-state misadjustment. By using a convex combination of two RLS algorithms with different forg...

A model-based least-squares algorithm with a time-varying forgetting factor is developed for leak detection in boiler steam-water systems. The algorithm has been tested using real industrial data from Syncrude Canada, and has proven to be effective in detection of boiler tube or steam leaks; proper implementation of the algorithm would lead to early leak warning, which is important in maintaini...

This note presents the basic recursive least squares (RLS) algorithm for recursively estimating parameters in linear regression models. In order to track time varying parameters a forgetting factor and a Kalman filter method are described. These methods may be used in some of the extra tasks in the project and is also used in the compulsory computer exercise (beräkningslaboration) No 5 . This n...