System Identification of Inventory System Using ARX and ARMAX Models

Rachad, Sofia; Nsiri, Benayad; Bensassi, Bahloul; Hassan, Ain Chock

doi:10.14257/ijca.2015.8.12.26

Cited by 26 publications

(18 citation statements)

References 8 publications

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“…Thus, in the current study, we sought to model and control macrophage pro-inflammatory activity, measured by iNOS expression. Using an ARX model structure, which is widely used for black-box system identification in engineering 31 and biological systems 30,33-36 , we identified computational models able to predict and control temporal iNOS expression. This black-box approach enabled us to fit three parameters to model the dynamic LPS response and three more to fit the IFN- response, in contrast to dozens required in mechanistic differential equation models of macrophage polarization 23 .…”

Section: Discussionmentioning

confidence: 99%

“…Grey and black box models, which capture dominant response dynamics without specifying mechanistic details, are thus more appealing to relate iNOS dynamics to pro-inflammatory stimulation 30 . We therefore sought to identify an optimized black box single input and single output (SISO) model relating LPS input to iNOS output 30,31 . A critical tradeoff must be considered when choosing model structure: maximize flexibility to best capture system dynamics while avoiding the need to have more model parameters than can be reliably identified from the data 32 .…”

Section: Auto-regressive Model With Exogenous Inputs Fits Inos Dynamimentioning

confidence: 99%

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Experimental control of macrophage pro-inflammatory dynamics using predictive models

Weinstock

Forsmo

Wilkinson

et al. 2019

Preprint

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Macrophage activity is a major component of the healthy response to infection and injury that consists of tightly regulated early pro-inflammatory activation followed by anti-inflammatory and regenerative activity. In numerous diseases, however, macrophage polarization becomes dysregulated and can not only impair recovery, but can also promote further injury and pathogenesis, e.g. after injury, in diabetes, or in Alzheimer's disease. Dysregulated macrophages may either fail to polarize or become chronically polarized, resulting in increased production of cytotoxic factors, diminished capacity to clear pathogens, or failure to promote tissue regeneration.In these cases, a method of predicting and dynamically controlling macrophage polarization will enable a new strategy for treating diverse inflammatory diseases. In this work, we developed a model-predictive control framework to temporally regulate macrophage polarization. Using RAW 264.7 macrophages as a model system, we enabled temporal control by identifying transfer function models relating the polarization marker iNOS to exogenous pro-and anti-inflammatory stimuli. These stimuli-to-iNOS response models were identified using linear autoregressive with exogenous input terms (ARX) equations and were coupled with nonlinear elements to account for experimentally identified supra-additive and hysteretic effects. Using this model architecture, we were able to reproduce experimentally observed temporal iNOS dynamics induced with lipopolysaccharides (LPS) and interferon gamma (IFN-γ). Moreover, the identified model enabled the design of time-varying input trajectories to experimentally sustain the duration and magnitude of iNOS expression. By designing transfer function models with the intent to predict cell behavior, we were able to predict and experimentally obtain temporal regulation of iNOS expression using LPS and IFN-γ from both naïve and non-naïve initial states. Moreover, our data driven models revealed decaying magnitude of iNOS response to LPS stimulation over time that could be recovered using combined treatment with both LPS and IFN-. Given the importance of dynamic tissue macrophage polarization and overall inflammatory regulation to a broad number of diseases, the temporal control methodology presented here will have numerous applications for regulating immune activity dynamics in chronic inflammatory diseases.3

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Section: Discussionmentioning

confidence: 99%

Section: Auto-regressive Model With Exogenous Inputs Fits Inos Dynamimentioning

confidence: 99%

Experimental control of macrophage pro-inflammatory dynamics using predictive models

Weinstock

Forsmo

Wilkinson

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…In 2014, Rachad et al, identify a production system, using the ARX model [3]. A second work was carried out for the identification of the production systems by making a comparison between the two models ARX and ARMAX [4].…”

Section: Introductionmentioning

confidence: 99%

Modeling of a Clothing Assembly line through the Identification Process

LARABI*¹,

RACHAD²,

Nsiri³

et al. 2019

IJRTE

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Nowadays, a new concept called fast fashion emerged worldwide. In order to remain efficient, companies need to keep their logistics under control. For that purpose, they need to control their manufacturing and decrease their delays and costs. To achieve that, industries need to make a lot of experiments before optimizing their supply chains. This may lead to unnecessary costs and it is contrary to the company’s policy. To overcome this situation, modeling and simulating could provide the best economical solution. The aim of this work is to obtain a mathematical model of an assembly line in the textile and clothing industry. The system will be represented by a black box with a real set of data collected from the industry. This model will have to reflect as faithfully as possible the studied system.

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“…The objective study of time series fuzzy models is how to improve forecasting accuracy by controlling uncertainty and involving fuzzy number support [10]- [12]. Conventionally, researchers use traditional analysis, modeling and forecasting methods such as AR (Autoregressive with exogenous input) model [13]. Approaches Autoregressive with exogenous inputs (ARX) and artificial neural network (ANN) models for the detection and imputation of anomalies in time series data are used to extract the characteristics of time series [14], In addition there is a proposed a two-stage weighted-least-squares regression approach, in which the prediction method includes a combination of two separate time-indexed ARX models to improve the prediction accuracy of the cooling load over different forecasting periods [15], [15].…”

Section: Introductionmentioning

confidence: 99%

Modeling of time series data for forecasting the number of foreign tourists in east Kalimantan using fuzzy inference system based on ARX model

Keliwar¹,

Putra²,

Hammad³

et al. 2018

IJET

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The government agencies require accurate tourism demand forecasts to plan the required tourism infrastructure, such as accommodation location planning and transportation development. Tourism demand forecasts can be viewed from various factors, one of which is the number of tourists per period. Without ignoring the number of domestic tourists, the increasing number of foreign tourists is prioritized by the government to increase the country's foreign exchange. Usually, a tourist destination is to visit some tour objects and need some accommodation and hotel sites to rest. With this consideration, the forecasting number of foreign tourists can be done by using data on the number of tour objects, accommodation and hotel sites, foreign and domestic tourists from the previous period. Data on the number of domestic tourists used to measure the tendency of foreign tourists compared with domestic tourists to all existing tour objects. All data history can be viewed as time series data. Conventionally, many researchers have employed traditional methods of time series analysis, modeling, and forecasting such as ARX (Autoregressive with exogenous input) model. FIS (Fuzzy Inference System) is a system that processes the input mapping formulation provided to produce output using Fuzzy Logic. The aim of this study is to forecast the number of foreign tourists by using FIS through a training process conducted by adapting the number of linguistic variables. All the training data are modeled by using the ARX model.

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System Identification of Inventory System Using ARX and ARMAX Models

Cited by 26 publications

References 8 publications

Experimental control of macrophage pro-inflammatory dynamics using predictive models

Experimental control of macrophage pro-inflammatory dynamics using predictive models

Modeling of a Clothing Assembly line through the Identification Process

Modeling of time series data for forecasting the number of foreign tourists in east Kalimantan using fuzzy inference system based on ARX model

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