Decomposition of the multi-dimensional time series identification problem

Klimchenko, V.V.

doi:10.1134/s000511790805010x

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…At this stage, the filter F k−1 (q −1 ), found at the (k−1)th stage, is used in order to transform the matrix G k (q −1 ) = F k (q −1 )F k T (q) so that its transform contains nonzero elements in only one line, one column, and on the main diagonal. This technique substantially simplifies the procedure of spectral factorization (finding the matrix function F k (q −1 )) [11].…”

Section: Filter Designmentioning

confidence: 99%

“…Simultaneously, the model for the transfer matrix of the inverse filter F 0 −1 (q −1 ), which transforms the initial time series into the white noise, is also found. The algorithm for constructing both the shaping filter F 0 (q −1 ) and its inverse F 0 −1 (q −1 ) is described in [11]. Based on this algorithm, the sequence of prediction errors ε t should be N-dimensional white noise.…”

Section: Filter Designmentioning

confidence: 99%

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Multi-Output Soft Sensor with a Multivariate Filter That Predicts Errors Applied to an Industrial Reactive Distillation Process

et al. 2021

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The paper deals with the problem of developing a multi-output soft sensor for the industrial reactive distillation process of methyl tert-butyl ether production. Unlike the existing soft sensor approaches, this paper proposes using a soft sensor with filters to predict model errors, which are then taken into account as corrections in the final predictions of outputs. The decomposition of the problem of optimal estimation of time delays is proposed for each input of the soft sensor. Using the proposed approach to predict the concentrations of methyl sec-butyl ether, methanol, and the sum of dimers and trimers of isobutylene in the output product in a reactive distillation column was shown to improve the results by 32%, 67%, and 9.5%, respectively.

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Section: Filter Designmentioning

confidence: 99%

Section: Filter Designmentioning

confidence: 99%

Multi-Output Soft Sensor with a Multivariate Filter That Predicts Errors Applied to an Industrial Reactive Distillation Process

et al. 2021

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Fractal Approach, Bifurcation Poker and Suc-Logic for Nonlinear Dynamics Forecasting

Kolokolov

Monovskaya

2013

Int. J. Bifurcation Chaos

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The paper is devoted to the novel logic (SUC-logic) of the nonlinear dynamics forecasting. The SUC-logic is based on three main points: the special sections (S) to build the 2D projections of multidimensional spaces without the loss of useful information; the special units (U) of measurement to estimate the nonlinear dynamics evolution; the special consecutions (C) to realize the nonlinear dynamics forecasting step-by-step. The fractal approach to forecasting the nonlinear dynamics in real-time together with the approach to build the modified bifurcation diagrams to research the regularities and the uncertainties of the evolution scenarios are developed with the SUC-logic. The physical meaning of the uncertainty zone, the stability margin, the risk estimation, the farthest forecasting and the earliest forecasting are considered from the viewpoint of the nonlinear dynamics aspect. Reasonings and discussions are based on experimental and computational results.

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Decomposition of the multi-dimensional time series identification problem

Cited by 2 publications

References 7 publications

Multi-Output Soft Sensor with a Multivariate Filter That Predicts Errors Applied to an Industrial Reactive Distillation Process

Multi-Output Soft Sensor with a Multivariate Filter That Predicts Errors Applied to an Industrial Reactive Distillation Process

Fractal Approach, Bifurcation Poker and Suc-Logic for Nonlinear Dynamics Forecasting

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