Transfer function identification from phase response data

Tommasi, Luciano De; Deschrijver, Dirk; Dhaene, Tom

doi:10.1016/j.aeue.2008.12.006

Cited by 9 publications

(3 citation statements)

References 7 publications

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“…Digital prediction filters may be derived as an extrapolated form of Newton’s series (Brown, 1972) or more generally from arbitrary high-pass filters, such as the Chebyshev form (Vaidyanathan, 2008). Filter design techniques that seek to fit a filter form to the ideal prediction filter characteristic may also be utilized (De Tommasi et al , 2010). In general, the digital prediction filter H p ( z ) has an associated high-pass filter H Hp ( z ), where: …”

Section: Prediction Filtersmentioning

confidence: 99%

Analysis of feedback predictive encoder based ADCs

Scanlan

O’Hare

Halton

et al. 2017

COMPEL

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ABSTRACT:Purpose-The purpose of this paper is to present analysis of the feedback predictive encoder based ADC (Analog-to-Digital Converter).Design/methodology/approach-The use of feedback predictive encoder based ADCs presents an alternative to the traditional two stage pipeline ADC by replacing the input estimate producing first stage of the pipeline, with a predictive loop that also produces an estimate of the input signal. Findings-The overload condition for feedback predictive encoder ADCs is dependent oninput signal amplitude and frequency, system gain and filter order. The limitation on the practical useable filter order is set by limit cycle oscillation. A boundary condition is defined for determination of maximum useable filter order. In a practical implementation of the predictive encoder ADC, the time allocated to the key functions of the gain stage and loop quantizer leads to optimisation of the power consumption.Originality/value-This paper presents a methodology to optimise the bandwidth of predictive encoder ADCs. The overload and stability conditions may be used to determine the maximum input signal bandwidth for a given loop quantizer. Optimisation of power consumption based on the allocation of time between the gain stage and the SAR ADC operation is investigated.The lower bound of power consumption for this architecture is estimated. Paper Type-Research Paper

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Section: Prediction Filtersmentioning

confidence: 99%

Analysis of feedback predictive encoder based ADCs

Scanlan

O’Hare

Halton

et al. 2017

COMPEL

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“…Examples of frequency-sampled data are scattering parameters (S-parameters) for RF objects and admittance parameters (Y -parameters) for interconnects. Alternative data choices, such as frequency response derivative H ′ (s) [20], phase response ∠H (s) [21] and magnitude response |H (s)| [22], are used for different identification purposes. In practices, frequency-domain macromodeling involves complicated measurements.…”

Section: Data: Input Data Choicesmentioning

confidence: 99%

A Decade of Vector Fitting Development: Applications on Signal∕Power Integrity

Lei

Wang

Chen

et al. 2010

AIP Conference Proceedings

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Vector Fitting (VF) has been introduced as a partial-fraction basis response fitting methodology for over a decade. Because of its reliability and versatility, VF has been applied and extended to a number of areas. In this book chapter, we will discuss the applications of VF in the context of macromodeling of linear structures in signal/power integrity analyses. We will also discuss main features of VF along three directions: data, algorithms and models. Two practical examples are given to demonstrate the merits of VF. An alternative P-norm approximation criterion is proposed to enhance the accuracy of the macromodeling process.

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“…By means of these measured data, applying some optimisation methods can obtain the system model. For example, Tommasi et al [15] considered the problem of transfer function identification from phase response data with prescribed frequency variation; Xu presented a damping iterative parameter estimation algorithm for the transfer function of the second-order system by using the frequency response data [16,17] and a Newton iterative parameter estimation algorithm for dynamical systems [18].…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation algorithms for dynamical response signals based on the multi‐innovation theory and the hierarchical principle

Ding

2017

IET signal process.

127

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In this study, the authors consider the parameter estimation problem of the response signal from a highly non-linear dynamical system. The step response experiment is taken for generating the measured data. Considering the stochastic disturbance in the industrial process and using the gradient search, a multi-innovation stochastic gradient algorithm is proposed through expanding the scalar innovation into an innovation vector in order to obtain more accurate parameter estimates. Furthermore, a hierarchical identification algorithm is derived by means of the decomposition technique and interaction estimation theory. Regarding to the coupled parameter problem between subsystems, the authors put forward the scheme of replacing the unknown parameters with their previous parameter estimates to realise the parameter estimation algorithm. Finally, several examples are provided to access and compare the behaviour of the proposed identification techniques.

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Transfer function identification from phase response data

Cited by 9 publications

References 7 publications

Analysis of feedback predictive encoder based ADCs

Analysis of feedback predictive encoder based ADCs

A Decade of Vector Fitting Development: Applications on Signal∕Power Integrity

Parameter estimation algorithms for dynamical response signals based on the multi‐innovation theory and the hierarchical principle

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