Model‐Based Signal Processing

Candy, James V.

doi:10.1002/0471732672

Cited by 103 publications

(137 citation statements)

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“…Various techniques can be implemented in these conditions, for example the use of proper process noise covariance matrix (separate from the control noise covariance) to account for model mismatches. In any case, obviously, the more the algorithm model differs from the real one, the more biased the forecasting will be [35].…”

Section: Resultsmentioning

confidence: 99%

Assessing the Quality of Natural Gas Consumption Forecasting: An Application to the Italian Residential Sector

Scarpa

Bianco

2017

Energies

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(1) Background: The present paper aims at estimating the quality of the forecasts obtained by using one equation models. In particular, the focus is on the effect that the explanatory variables have on the forecasted quantity. The analysis is performed on the long term natural gas consumption in the Italian residential sector, but the same methodology can be applied to other contexts; (2) Methods: Different ex ante knowledge scenarios are built by associating different levels of confidence to the same set of explanatory variables. Forecasting results, coming from a standard regression algorithm and confirmed by a Kalman filter, are analyzed by means of covariance matrix propagation to assess the quality of the provided estimates; (3) Results: The outcomes show that one-equation models are very sensitive to the quality of the explanatory variables, therefore their erroneous estimation may have a relevant detrimental effect on the predictive accuracy of the model; (4) Conclusions: The overall ex ante forecasting accuracy of an example of one equation model is assessed. It has emerged that long-term forecasts need particular attention when the covered time horizon spans over decades. The information contained in the present paper is of interest for energy planners, supply network managers and policy makers in order to support their decisions.

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Section: Resultsmentioning

confidence: 99%

Assessing the Quality of Natural Gas Consumption Forecasting: An Application to the Italian Residential Sector

Scarpa

Bianco

2017

Energies

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“…It can be shown [5,8,9] that the optimum noise canceling structure can be formulated as an identification problem with the noise reference input z(t) related to the ship noise estimateη(t) by a coloring filter h(t), i.e.,η…”

Section: Ship Noise Modelmentioning

confidence: 99%

“…That is, the nonlinearity of the measurement equations necessitates the use of the EKF [5], which in turn, requires the Jacobian.…”

Section: Joint Detection/noise Cancelationmentioning

confidence: 99%

“…Since such problems are, in general, nonstationary in character, the solution must be adaptive, and therefore the approach taken here is to cast it as a recursive model-based problem. By model-based [5] we mean that as much a priori information as possible is included in the form of physical models, where the parameters of these models can be adaptively estimated. Such an approach leads naturally to state-space based recursive structures.…”

Section: Introductionmentioning

confidence: 99%

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Sequential Detection Estimation and Noise Cancelation

Sullivan

Candy

Imaging for Detection and Identification

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Abstract. A noise canceling technique based on a model-based recursive processor is presented. Beginning with a canceling scheme using a reference source, it is shown how to obtain an estimate of the noise, which can then be incorporated in a recursive noise canceler. Once this is done, recursive detection and estimation schemes are developed. The approach is to model the nonstationary noise as an autoregressive process, which can then easily be placed into a state-space canonical form. This results in a Kalman-type recursive processor where the measurement is the noise and the reference is the source term. Once the noise model is in state-space form, it is combined with detection and estimation problems in a self-consistent structure. It is then shown how parameters of interest, such as a signal bearing or range, can be enhanced by including (augmenting) these parameters into the state vector, thereby jointly estimating them along with the recursive updating of the noise model.

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“…Assim, é viável a utilização do componente Workload Engine para realizar uma estimativa baseada no histórico dos tempos, tais como as predições feitas por QRSM (Myers et al, 2009) e ARMAX (Candy, 2006). Esses dois trabalhos fornecem estimativas adequadas para traces e cargas de trabalho reais.…”

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Oferecimento de QoS para computação em nuvens por meio de metaescalonamento

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Model‐Based Signal Processing

Cited by 103 publications

References 0 publications

Assessing the Quality of Natural Gas Consumption Forecasting: An Application to the Italian Residential Sector

Assessing the Quality of Natural Gas Consumption Forecasting: An Application to the Italian Residential Sector

Sequential Detection Estimation and Noise Cancelation

Oferecimento de QoS para computação em nuvens por meio de metaescalonamento

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