System identification and parameter estimation are important to obtain information from systems which are difficult to model and that are usually presented as BlackBox models. This work presents a point to point parameter estimation of a generalized non-deterministic system, whose results are variable through time, by using an exponential Forgetting Factor (FF). An average approximation is used...

A least-mean-square (LMS) and a recursive-least-square (RLS) algorithm are derived for estimation of the symbol period in communication signals. The algorithms are based on measurements of the time elapsed between two consecutive transitions detected in noisy signals. Number of symbol periods between the transitions is estimated too. In order to have the number equal to the true one, the initia...

This paper presents distributed conjugate gradient (CG) algorithms for distributed parameter estimation and spectrum estimation over wireless sensor networks. In particular, distributed conventional conjugate gradient (CCG) and modified conjugate gradient (MCG) algorithms are developed with incremental and diffusion adaptive cooperation strategies. The distributed CCG and MCG algorithms have an...

The paper aims to identify and control the coupled mass-spring-damper system. A nonlinear discrete polynomial structure is elaborated. Its parameters are estimated using Recursive Least Squares (RLS) algorithm. Moreover, a feedback stabilizing control law based on Kronecker power is designed. Finally, simulations are presented to illustrate the effectiveness of the proposed structure. Keywords—...

We obtain upper and lower bounds for the H" norm of the RLS (Recursive-Least-Squares) algorithm. The H" norm may be regarded aa the worst-case energy gain from the disturbances to the prediction errors, and is therefore a measure of the robustness of an algorithm to perturbations and model uncertainty. Our results allow one to compare the robustness of RLS compared to the LMS (Least-Mean-Square...

This article investigates how to adaptively predict the time-varying metrology delay that could realistically occur in the semiconductor manufacturing practice. Metrology delays pose a great challenge for the existing run-to-run (R2R) controllers, driving the process output significantly away from target if not adequately predicted. First, the expected asymptotic double exponentially weighted m...

Abstract In order to reduce the bit error rate and improve anti-interference performance, this paper proposes a channel estimation algorithm based on RLS. RLS obtains current data by modifying final data. First, first is estimated least squares (LS), then date adjusted in real time through forgetting factor recursive formula obtain all The results show that can computation, accuracy of estimati...

A bank of recursive least-squares (RLS) estimators is proposed for the estimation of the uncertainty intervals of the parameters of an equation error model (or RLS model) where the equation error is assumed to lie between known upper and lower bounds. It is shown that the off-line least-squares method gives the maximum and minimum parameter values that could have produced the recorded input-out...

In this paper we use kernel-based Fisher Discriminants (KFD) for classification by integrating this method in a HMM-based speech recognition system. We translate the outputs of the KFD-classifier into conditional probabilities and use them as production probabilities of a HMM-based decoder for speech recognition. To obtain a good performance also in terms of computational complexity the Recursi...

this paper presents new sensitivity-based methods for detection of structural damage using incomplete noisy modal data. these methods are based on the first-order derivative of modal parameters. changes of natural frequency do not usually provide spatial information on the structural damage. they are also not sensitive to the local damage. in this paper, a new sensitivity function is proposed u...