A review on robust M-estimators for regression analysis

Menezes, D. Q. F.; Prata, Diego Martinez; Secchi, Argimiro Resende; Pinto, José Carlos

doi:10.1016/j.compchemeng.2021.107254

Cited by 95 publications

(48 citation statements)

References 89 publications

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“…The cost function F corresponds to the maximum likelihood principle assuming a priori that measurements error follows the Normal (Gaussian) distribution [32]. Although it is the most used estimator in data regression problems, it is not robust against the less frequent outliers to deal with them, avoiding poor estimates over states and parameters for real applications; a robust m-estimator could be used as recently reviewed [43]. Robust m-estimators, which are generalizations of maximum-likelihood estimators and originating from robust statistics [32], have been examined in numerous areas of science (since 1888).…”

Section: Training Algorithmmentioning

confidence: 99%

“…Robust m-estimators, which are generalizations of maximum-likelihood estimators and originating from robust statistics [32], have been examined in numerous areas of science (since 1888). Samples are presented and tuning for 90%, 95%, 98%, and 99% relative efficiency levels regarding the Normal distribution for regression analysis is performed [43]. In particular, this regression problem is described by a nonlinear model in the state and parameter space (not expressed by high-nonlinear unbounded functions, e.g., exponentials one from chemical reactions models).…”

Section: Training Algorithmmentioning

confidence: 99%

“…Analyzing this plot, we deal with 0.004601, which shows that the training dataset errors lie underneath 5, the validation and testing lie below 6 and 7, and the Mean Square Error (MSE) was 2.78773 × 10 −6 . As discussed earlier, the negative effect of outliers on estimates in regression problems can be realized using robust m-estimators [32,43], including, adaptations (a weight vector for each observation) to the EKF for robust state estimation problems [28] and the robust approach should be addressed in future works. Figure 13 shows the error auto-correlation function for the NARX, interlinking the time relationship of the forecasted errors.…”

Section: Bode Diagrammentioning

confidence: 99%

See 2 more Smart Citations

Memristive Equivalent Circuit Model for Battery

Mogadem

2021

Sustainability

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The design of mathematical models is based on conservation laws and also on the fundamental principles of modeling: structure, parameters, and physical meaning. Those kinds of modeling should have specific capabilities to deal with different working conditions and environments coping with challenges that include but are not limited to battery capacity, life-cycle, or the attempts to manipulate the current profiles during operation. Introducing memristive elements in batteries will be ideal to satisfy these fundamentals and goals of modeling, whereas addressing the recycling and sustainability concerns on the environmental impact by the placement of TiO2 memristor into this model can promote a recovery hierarchy via recycling and dispatching a slight amount to disposal as the previous focus was mainly concentrated on availability. As for battery materials, modeling, performing, and manufacturing all have proliferated to grasp the possible sustainability challenges inherited in these systems. This paper investigated electrochemical impedance spectroscopy to study this model and the dynamic behavior inside the battery. We found a solution to address the existing battery limitations that elucidate the battery degradation without affecting the performance, correspondingly by employing the dampest least-squares combination with nonlinear autoregressive exogenous for identifying such model and its associated parameters because of its embedded memory and fast convergence to diminish the influence of the vanishing gradient. Lastly, we found that this model is proven to be efficient and accurate compared to actual experimented data to validate our theory and show the value of the proposed model in real life while assuming Normal Gaussian distribution of data error with outstanding results; the auto-correlations were within the 95% confidence limit, the best validation was 2.7877, and an overall regression of 0.99993 was achieved.

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Section: Training Algorithmmentioning

confidence: 99%

Section: Training Algorithmmentioning

confidence: 99%

Section: Bode Diagrammentioning

confidence: 99%

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Memristive Equivalent Circuit Model for Battery

Mogadem

2021

Sustainability

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“…However, it should be noted that the cost of more accurate instruments often increases exponentially with higher accuracy. Another, cheaper and at the same time effective method of improving the results is to reconcile them using the technology of data validation and reconciliation [11][12][13][14]. Another cheaper and, at the same time, effective method of improving the results involves their reconciliation with the use of data validation and reconciliation.…”

Section: Introductionmentioning

confidence: 99%

“…During the same period, numerous interesting research studies were published in world literature concerning both general principles involving the application of data validation and reconciliation [11,12,14,[26][27][28][29][30] as well as its application for solving more specific problems. For example, they were presented in the publications covering the problems of nuclear power plants [31][32][33], chemical industry [34][35][36][37][38][39][40], power plants and combined heat and power plants [41][42][43][44][45][46], refrigeration [47], and biotechnology [48].…”

Section: Introductionmentioning

confidence: 99%

Application of Data Validation and Reconciliation to Improve Measurement Results in the Determination Process of Emission Characteristics in Co-Combustion of Sewage Sludge with Coal

Kozioł

2021

Sustainability

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One of the actions popularized worldwide to reduce the consumption of fossil fuels is the combustion of renewable fuels and the co-combustion of both of these fuels. To properly implement combustion and co-combustion processes in power-generation installations, operational characteristics, including emission characteristics are required. To determine these characteristics, tests must be conducted, within the scope of which, for individual operating stages of the installation’s work, the readings collected from a relatively large number of control and measurement instruments should be taken into account. All these instruments have different levels of accuracy, which, among other factors, bring about lower adequacy of the characteristics determined on the basis of these measurements. The objective of this study is to present possible adaptations of data validation and reconciliation methods to increase the adequacy of emission characteristics for the process of co-combustion of fuels. The methodology is discussed based on the example of studies on the co-combustion process of sewage sludge with coal in a grate furnace. The aforementioned characteristics were determined based on measurement tests of gaseous emissions of flue gas components. The tests were carried out for various preset operational conditions of the process, such as the thickness of fuel layer on the grate, the share of sludge in the fuel, the humidity of the sludge, the theoretical ratio of excess air to combustion, and the distribution of air stream during the process. The research object is described and detailed research results concerning two exemplary measurement tests are given, as well as the most important results referring to the whole research. The performed calculations indicate the necessity to take into account often significant corrections, which can amount to about 10% of the measured value.

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Digital Twin for the SBR Cold Emulsion Copolymerization Process

Carpio,

Feital,

Câmara

et al. 2024

Macro Reaction Engineering

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Development of accurate and robust dynamic models for description of emulsion copolymerization processes used for production of styrene/1,3‐butadiene rubber (SBR), the most widely used elastomer among all the synthetic rubbers produced globally, is fundamental for implementation of monitoring, advanced control, and optimization strategies. There are several studies on dynamic modeling of SBR emulsion copolymerization, but most of them focus on hot conditions or only one semibatch reactor, as in the case of cold conditions. For this reason, the present study focuses on dynamic modeling of SBR cold emulsion copolymerization processes considering a train of 15 continuous stirred tank reactors, as in many real industrial sites. The developed dynamic model is implemented by using the Digital Twin (DT) concept, that involves the online reading of process variables and an adaptive strategy for online tuning of some of the model parameters, being also sensitive to the effect of real‐time changes to the number of reactors in the train, a subject that has been overlooked previously, but which is important at plant site. The practical application of the DT for monitoring a real industrial process illustrates the robustness and accuracy of the developed tool, making it useful for opportune detection of process anomalies and opening the way for future advanced control strategies.This article is protected by copyright. All rights reserved

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A review on robust M-estimators for regression analysis

Cited by 95 publications

References 89 publications

Memristive Equivalent Circuit Model for Battery

Memristive Equivalent Circuit Model for Battery

Application of Data Validation and Reconciliation to Improve Measurement Results in the Determination Process of Emission Characteristics in Co-Combustion of Sewage Sludge with Coal

Digital Twin for the SBR Cold Emulsion Copolymerization Process

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