3,929 research outputs found
Recursive least squares for online dynamic identification on gas turbine engines
Online identification for a gas turbine engine is vital for health
monitoring and control decisions because the engine electronic
control system uses the identified model to analyze the performance
for optimization of fuel consumption, a response to the pilot
command, as well as engine life protection. Since a gas turbine engine
is a complex system and operating at variant working conditions, it
behaves nonlinearly through different power transition levels and at
different operating points. An adaptive approach is required to capture
the dynamics of its performance
Underdetermined-order recursive least-squares adaptive filtering: The concept and algorithms
Published versio
Recursive Least Squares Filtering Algorithms for On-Line Viscoelastic Characterization of Biosamples
The mechanical characterization of biological samples is a fundamental issue in biology
and related fields, such as tissue and cell mechanics, regenerative medicine and diagnosis of diseases.
In this paper, a novel approach for the identification of the stiffness and damping coefficients
of biosamples is introduced. According to the proposed method, a MEMS-based microgripper
in operational condition is used as a measurement tool. The mechanical model describing the
dynamics of the gripper-sample system considers the pseudo-rigid body model for the microgripper,
and the Kelvin–Voigt constitutive law of viscoelasticity for the sample. Then, two algorithms based
on recursive least square (RLS) methods are implemented for the estimation of the mechanical
coefficients, that are the forgetting factor based RLS and the normalised gradient based RLS
algorithms. Numerical simulations are performed to verify the effectiveness of the proposed approach.
Results confirm the feasibility of the method that enables the ability to perform simultaneously two
tasks: sample manipulation and parameters identification
A state space forecasting model with fiscal and monetary control
In this paper we model the U.S. economy parsimoniously in an a theoretic state space representation. We use monthly data for thirteen macroeconomic variables. We treat the federal deficit as a proxy for fiscal policy and the fed funds rate as a proxy for monetary policy and use each of them as control (exogenous) variables, and designate the rest as state variables. The output (measured) variable is the growth rate of quarterly real GDP which we interpolate to obtain a monthly equivalent. We specify a linear relation between state variables and implicitly allow for time variation of the relationship by using a recursive least squares (RLS) with forgetting factor algorithm to estimate the coefficients. The model coefficients are also estimated using ordinary least squares (OLS) and the resulting forecasts (in-sample and out-of-sample) are compared. The RLS algorithm performs better in the out-of-sample forecasts, particularly for those state variables which exhibit the greatest cyclical variations. Variables which had greater stability were forecasted more precisely with OLS estimated parameters.Forecasting ; Econometric models
Discrete-time variance tracking with application to speech processing
Two new discrete-time algorithms are presented for tracking variance and reciprocal variance. The closed
loop nature of the solutions to these problems makes this approach highly accurate and can be used
recursively in real time. Since the Least-Mean Squares (LMS) method of parameter estimation requires an
estimate of variance to compute the step size, this technique is well suited to applications such as speech
processing and adaptive filtering
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