Estimating Parameters of a Sine Wave by Separable Nonlinear Least Squares Fitting

Chen, Kui Fu

doi:10.1109/tim.2010.2047298

Cited by 36 publications

(11 citation statements)

References 23 publications

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“…There were many studies in the oscillatory or sinusoidal models, especially in analysis and modeling speech signal and audio [20]- [22]. Chen [23] discussed the estimation of parameters of the stationary sinusoidal model by deriving the nonlinear equation as a function of frequency and solving it using Newton method. At the same time, Pantazis et al [21] discussed a time-varying quasi-harmonic model (QHM) by presenting the sinusoidal signal as a sum of products of polynomials and exponentials and estimating the parameters by iterative procedure with considering the frequency mismatch.…”

Section: A R T I C L E I N F Omentioning

confidence: 99%

Estimating the function of oscillatory components in SSA-based forecasting model

Sulandari¹,

Subanar²,

Suhartono

et al. 2019

Int. J. Adv. Intell. Informatics

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The study of SSA-based forecasting model is always interesting due to its capability in modeling trend and multiple seasonal time series. The aim of this study is to propose an iterative ordinary least square (OLS) for estimating the oscillatory with time-varying amplitude model that usually found in SSA decomposition. We compare the results with those obtained by nonlinear least square based on Levenberg Marquardt (NLM) method. A simulation study based on the time series data which has a linear amplitude modulated sinusoid component is conducted to investigate the error of estimated parameters of the model obtained by the proposed method. A real data series was also considered for the application example. The results show that in terms of forecasting accuracy, the SSA-based model where the oscillatory components are obtained by iterative OLS is nearly the same with that is obtained by the NLM method.

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Section: A R T I C L E I N F Omentioning

confidence: 99%

Estimating the function of oscillatory components in SSA-based forecasting model

Sulandari¹,

Subanar²,

Suhartono

et al. 2019

Int. J. Adv. Intell. Informatics

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“…Input current applied on the damper are from 0 to 1 Ampere. Nonlinear least square fitting method is used for parameter estimation using MATLAB [14]. The parameter is fitted with different input current, and errors from all models are compared to determine the most suitable model to represent the behavior of MR damper.…”

Section: Parameter Estimation For Varying Currentmentioning

confidence: 99%

Estimation of Parameter for Different Magnetorheological Fluids Model for Varying Current

Razali¹,

Structures²,

Muthalif³

et al. 2018

IJCEE

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Magnetorheological fluids (MRF) are considered as smart material since the behavior of the fluid is interrelated with external magnetic field. The rheological behavior of the fluids is changed whenever the strength of magnetic field changed. One of the applications of MRF is Magnetorheological damper (MRD), a damper that can be controlled by magnetic field. Analytical model of the damper is important in order to understand the performance of MRD. Different types of model that depend on defined parameters were introduced by previous researchers, and investigation on those parameters is crucial to acquire a model with least value of error. A technique called nonlinear least squares fitting method is utilized in order to obtain those parameters. This study investigated five different MRD models and presents the result for parameter estimation for each model as a function of input current.

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“…It can be used when the data set is noncoherently sampled [4], [9] or clipped, thus, relaxing two conditions for the spectral test (coherent sampling and in-range amplitude). The method includes estimating amplitudes of fundamental and harmonic components and evaluating the spectral parameters.…”

Section: Four-parameter Sine Fit (Fpsf)mentioning

confidence: 99%

A Comparative Study of State-of-the-Art High-Performance Spectral Test Methods

Sudani

Chen

2015

IEEE Des. Test

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h SPECTRAL ANALYSIS IS one of the most widely used methods in several areas such as geophysics, oceanography, medical sciences, etc. It provides information about different frequency components present in a signal that are otherwise not observed in time domain. In electronics, it is extensively used to test dynamic parameters of a wide range of semiconductor circuits and systems, such as analog-to-digital converters (ADCs).ADCs are one of the most widely used mixedsignal circuits. Sufficient spectral performance of ADCs is critical for high-speed and high-resolution applications such as communications [1], [2]. To ensure high accuracy in spectral test, the IEEE standards [3], [4] recommend the test setup to satisfy a list of stringent requirements.As ADC resolution and speed become high, the IEEE requirements have become challenging. Furthermore, satisfying these requirements inevitably increases test time and test cost. Relaxing these requirements not only decreases test time/cost but also facilitates on-chip test capability. Several methods have been proposed in the past that relax one or more of these requirements. However, it is not clear which method suits what applications.We provide a comparative study of four methods that relax stringent conditions for spectral test, based on several practical criteria. This enables the user to select the most suited method for his/her situation.Ideal DFT-based spectral test Figure 1 shows the ideal discrete Fourier transform (DFT)-based spectral test setup. The IEEE recommended requirements include the following.

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Estimating Parameters of a Sine Wave by Separable Nonlinear Least Squares Fitting

Cited by 36 publications

References 23 publications

Estimating the function of oscillatory components in SSA-based forecasting model

Estimating the function of oscillatory components in SSA-based forecasting model

Estimation of Parameter for Different Magnetorheological Fluids Model for Varying Current

A Comparative Study of State-of-the-Art High-Performance Spectral Test Methods

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