Joint order and parameter estimation of multivariate autoregressive models using multi-model partitioning theory

Pappas, Stylianos Sp.; Leros, Assimakis K.; Katsikas, Sokratis K.

doi:10.1016/j.dsp.2006.06.006

Cited by 13 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…. , (10,10) programmed with the MMPA. Then the MMPA's estimation error was applied as input to the SVM that were able to achieve a further error reduction and come up with a better forecasting outcome.…”

Section: Resultsmentioning

confidence: 99%

“…However, if the system model is not completely known the MMPA, introduced by Lainiotis [8,9], is one of the most widely used approaches for similar problems [10][11][12][13][14][15][16][17][18][19][25][26][27][28].…”

Section: Multimodel Partition Algorithm (Mmpa)mentioning

confidence: 99%

“…is a zero mean white process with covariance matrix Pỹ ( − 1; ) = H ( ; ) P ( ; ) H T ( ; ) + R. (10) For equations (8)-(10) = 1, 2, . .…”

Section: Multimodel Partition Algorithm (Mmpa)mentioning

confidence: 99%

“…This approach was introduced by Lainiotis [8,9] and summarizes the parametric model uncertainty into an unknown, finite dimensional parameter vector whose values are assumed to lie within a known set of finite cardinality. A nonexhaustive list of the reformulation, extension, and application of the MMPA approach as well as its application to a variety of problems can be found in [10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A New Hybrid Forecasting Strategy Applied to Mean Hourly Wind Speed Time Series

Pappas

Karamousantas

Chatzarakis

et al. 2014

Journal of Wind Energy

Self Cite

View full text Add to dashboard Cite

An alternative electric power source, such as wind power, has to be both reliable and autonomous. An accurate wind speed forecasting method plays the key role in achieving the aforementioned properties and also is a valuable tool in overcoming a variety of economic and technical problems connected to wind power production. The method proposed is based on the reformulation of the problem in the standard state space form and on implementing a bank of Kalman filters (KF), each fitting an ARMA model of different order. The proposed method is to be applied to a greenhouse unit which incorporates an automatized use of renewable energy sources including wind speed power.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Multimodel Partition Algorithm (Mmpa)mentioning

confidence: 99%

“…is a zero mean white process with covariance matrix Pỹ ( − 1; ) = H ( ; ) P ( ; ) H T ( ; ) + R. (10) For equations (8)-(10) = 1, 2, . .…”

Section: Multimodel Partition Algorithm (Mmpa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Hybrid Forecasting Strategy Applied to Mean Hourly Wind Speed Time Series

Pappas

Karamousantas

Chatzarakis

et al. 2014

Journal of Wind Energy

Self Cite

View full text Add to dashboard Cite

show abstract

“…Step 2_Normalised residues estimation: In the literature, different approaches were proposed to estimate validity degrees of sub-models [ 41 , 59 , 60 ] that can be defined by a switching function or by fusion of sub-model responses. In our study, we use a fusion technique based on computing normalised residues, err’ i1 and err’ i2 , of each sub-model to evaluate its pertinence to describe the system in an operating area at each time, estimation of errors between the real and the estimated outputs:

…”

Section: Methodsmentioning

confidence: 99%

Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals

2022

View full text Add to dashboard Cite

This paper develops a novel approach to characterise muscle force from electromyography (EMG) signals, which are the electric activities generated by muscles. Based on the nonlinear Hammerstein–Wiener model, the first part of this study outlines the estimation of different sub-models to mimic diverse force profiles. The second part fixes the appropriate sub-models of a multimodel library and computes the contribution of sub-models to estimate the desired force. Based on a pre-existing dataset, the obtained results show the effectiveness of the proposed approach to estimate muscle force from EMG signals with reasonable accuracy. The coefficient of determination ranges from 0.6568 to 0.9754 using the proposed method compared with a range of 0.5060 to 0.9329 using an artificial neural network (ANN), generating significantly different accuracy (p < 0.03). Results imply that the use of multimodel approach can improve the accuracy in proportional control of prostheses.

show abstract