Robustness analysis of a MOEA-based elicitation method for outranking model parameters

2013 ◽  
Author(s):  
Edgar Covantes ◽  
Eduardo Fernandez ◽  
Jorge Navarro
Keyword(s):  
Robustness Analysis ◽  
Model Parameters ◽  
Elicitation Method

scholarly journals Robustness Analysis of an Outranking Model Parameters’ Elicitation Method in the Presence of Noisy Examples

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Nelson Rangel-Valdez ◽  
Eduardo Fernandez ◽  
Laura Cruz-Reyes ◽  
Claudia Gomez-Santillan ◽  
Gilberto Rivera ◽  
...  
Keyword(s):  
Noise Level ◽  
Decision Aid ◽  
Robustness Analysis ◽  
Model Parameters ◽  
Multicriteria Decision Aid ◽  
Elicitation Method ◽  
Relational System ◽  
Reference Set ◽  
Preference Disaggregation ◽  
Noisy Examples

One of the main concerns in Multicriteria Decision Aid (MCDA) is robustness analysis. Some of the most important approaches to model decision maker preferences are based on fuzzy outranking models whose parameters (e.g., weights and veto thresholds) must be elicited. The so-called preference-disaggregation analysis (PDA) has been successfully carried out by means of metaheuristics, but this kind of works lacks a robustness analysis. Based on the above, the present research studies the robustness of a PDA metaheuristic method to estimate model parameters of an outranking-based relational system of preferences. The method is considered robust if the solutions obtained in the presence of noise can maintain the same performance in predicting preference judgments in a new reference set. The research shows experimental evidence that the PDA method keeps the same performance in situations with up to 10% of noise level, making it robust.

scholarly journals An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City

2021 ◽  
Author(s):  
Sheng Zhang ◽  
Joan Ponce ◽  
Zhen Zhang ◽  
Guang Lin ◽  
George Karniadakis
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Robustness Analysis ◽  
Data Driven ◽  
Model Parameters ◽  
Calibration Model ◽  
Epidemiological Models ◽  
Analysis Model ◽  
Identifiability Analysis

AbstractEpidemiological models can provide the dynamic evolution of a pandemic but they are based on many assumptions and parameters that have to be adjusted over the time when the pandemic lasts. However, often the available data are not sufficient to identify the model parameters and hence infer the unobserved dynamics. Here, we develop a general framework for building a trustworthy data-driven epidemiological model, consisting of a workflow that integrates data acquisition and event timeline, model development, identifiability analysis, sensitivity analysis, model calibration, model robustness analysis, and forecasting with uncertainties in different scenarios. In particular, we apply this framework to propose a modified susceptible–exposed–infectious–recovered (SEIR) model, including new compartments and model vaccination in order to forecast the transmission dynamics of COVID-19 in New York City (NYC). We find that we can uniquely estimate the model parameters and accurately predict the daily new infection cases, hospitalizations, and deaths, in agreement with the available data from NYC’s government’s website. In addition, we employ the calibrated data-driven model to study the effects of vaccination and timing of reopening indoor dining in NYC.

Robustness Analysis of a Simple and Augmented Proportional Plus Derivative Controller in Trajectory Following Robots Using the Floquet Theory

2018 ◽  
Vol 13 (7) ◽  
Author(s):  
B. Sandeep Reddy ◽  
Ashitava Ghosal
Keyword(s):  
Numerical Simulation ◽  
Degrees Of Freedom ◽  
Floquet Theory ◽  
Robustness Analysis ◽  
Periodic Trajectory ◽  
Model Parameters ◽  
Asymptotically Stable ◽  
Pd Controller ◽  
Simulation Results ◽  
Trajectory Following

This paper deals with the issue of robustness in control of robots using the proportional plus derivative (PD) controller and the augmented PD controller. In the literature, a variety of PD and model-based controllers for multilink serial manipulator have been claimed to be asymptotically stable for trajectory tracking, in the sense of Lyapunov, as long as the controller gains are positive. In this paper, we first establish that for simple PD controllers, the criteria of positive controller gains are insufficient to establish asymptotic stability, and second that for the augmented PD controller the criteria of positive controller gains are valid only when there is no uncertainty in the model parameters. We show both these results for a simple planar two-degrees-of-freedom (2DOFs) robot with two rotary (R) joints, following a desired periodic trajectory, using the Floquet theory. We provide numerical simulation results which conclusively demonstrate the same.

scholarly journals Robustness Analysis of Model Parameters for Sediment Transport Equation Development

2019 ◽  
Vol 12 ◽  
pp. 1-17
Author(s):  
Nadiatul Adilah Ahmad Abdul Ghani ◽  
Junaidah Ariffin ◽  
Duratul Ain Tholibon
Keyword(s):  
Sediment Transport ◽  
Transport Equation ◽  
Polynomial Regression ◽  
Robustness Analysis ◽  
Shear Velocity ◽  
Model Parameters ◽  
Evolutionary Polynomial Regression ◽  
Sediment Transport Equation ◽  
Sediment Data ◽  
Influential Parameters

Robustness analysis of model parameters for sediment transport equation development is carried out using 256 hydraulics and sediment data from twelve Malaysian rivers. The model parameters used in the analyses include parameters in equations by Ackers-White, Brownlie, Engelund-Hansen, Graf, Molinas-Wu, Karim-Kennedy, Yang, Ariffin and Sinnakaudan. Seven parameters in five parameter classes were initially tested. Robustness of the model parameters was measured on the statistical relations through Evolutionary Polynomial Regression (EPR) technique and further examined using the discrepancy ratio of the predicted versus the measured values. Results from analyses suggest  (ratio of shear velocity to flow velocity) and  (ratio of hydraulic radius to mean sediment diameter) to be the most significant and influential parameters for the development of sediment transport equation.

scholarly journals Mathematical Modeling and Robustness Analysis to Unravel COVID-19 Transmission Dynamics: The Italy Case

Biology ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 394
Author(s):  
Chiara Antonini ◽  
Sara Calandrini ◽  
Fabrizio Stracci ◽  
Claudio Dario ◽  
Fortunato Bianconi
Keyword(s):  
National Level ◽  
Robustness Analysis ◽  
Calibration Method ◽  
Model Parameters ◽  
Civil Protection ◽  
Bayesian Calibration ◽  
Computational Framework ◽  
Hospitalization Rates ◽  
Intervention Measures ◽  
Umbria Region

This study started from the request of providing predictions on hospitalization and Intensive Care Unit (ICU) rates that are caused by COVID-19 for the Umbria region in Italy. To this purpose, we propose the application of a computational framework to a SEIR-type (Susceptible, Exposed, Infected, Removed) epidemiological model describing the different stages of COVID-19 infection. The model discriminates between asymptomatic and symptomatic cases and it takes into account possible intervention measures in order to reduce the probability of transmission. As case studies, we analyze not only the epidemic situation in Umbria but also in Italy, in order to capture the evolution of the pandemic at a national level. First of all, we estimate model parameters through a Bayesian calibration method, called Conditional Robust Calibration (CRC), while using the official COVID-19 data of the Italian Civil Protection. Subsequently, Conditional Robustness Analysis (CRA) on the calibrated model is carried out in order to quantify the influence of epidemiological and intervention parameters on the hospitalization rates. The proposed pipeline properly describes the COVID-19 spread during the lock-down phase. It also reveals the underestimation of new positive cases and the need of promptly isolating asymptomatic and presymptomatic cases. The results emphasize the importance of the lock-down timeliness and provide accurate predictions on the current evolution of the pandemic.

scholarly journals Robustness Analysis of the Estimators for the Nonlinear System Identification

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 834
Author(s):  
Wiktor Jakowluk ◽  
Karol Godlewski
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Adaptive Filtering ◽  
Nonlinear System Identification ◽  
Robustness Analysis ◽  
Estimation Methods ◽  
Function Evaluation ◽  
Model Parameters ◽  
Maximum Information ◽  
Plant Model

The main objective of the system identification is to deliver maximum information about the system dynamics, while still ensuring an acceptable cost of the identification experiment. The focus of such an idea is to design an appropriate experiment so that the departure from normal working conditions during the identification task is minimized. In this paper, the adaptive filtering (AF) scheme for plant model parameter estimation is proposed. The experimental results are obtained using the nonlinear interacting water tanks system. The adaptive filtering is expressed by minimizing the error between the system and the plant model outputs subject to the white noise signal affecting system output. This measurement error is quantified by the comparison of Minimum Error Entropy Renyi (MEER), Least Entropy Like (LEL), Least Squares (LS), and Least Absolute Deviation (LAD) estimators, respectively. The plant model parameters were obtained using sequential quadratic programming (SQP) algorithm. The robustness tests for the double-tank water system parameter estimators are performed using the ellipsoidal confidence regions. The effectiveness analysis for the above-mentioned estimators relies on the total number of iterations and the number of function evaluation comparisons. The main contribution of this paper is the evaluation of different estimation methods for the nonlinear system identification using various excitation signals. The proposed empirical study is illustrated by the numerical examples, and the simulation results are discussed.

scholarly journals Uncovering the effects of heterogeneity and parameter sensitivity on within-host dynamics of disease: malaria as a case study

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shade Horn ◽  
Jacky L. Snoep ◽  
David D. van Niekerk
Keyword(s):  
Disease Model ◽  
Robustness Analysis ◽  
Model Parameters ◽  
Local Sensitivity ◽  
Model Predictions ◽  
Weak Points ◽  
Determination Of Parameters ◽  
Associated Variables

Abstract Background The fidelity and reliability of disease model predictions depend on accurate and precise descriptions of processes and determination of parameters. Various models exist to describe within-host dynamics during malaria infection but there is a shortage of clinical data that can be used to quantitatively validate them and establish confidence in their predictions. In addition, model parameters often contain a degree of uncertainty and show variations between individuals, potentially undermining the reliability of model predictions. In this study models were reproduced and analysed by means of robustness, uncertainty, local sensitivity and local sensitivity robustness analysis to establish confidence in their predictions. Results Components of the immune system are responsible for the most uncertainty in model outputs, while disease associated variables showed the greatest sensitivity for these components. All models showed a comparable degree of robustness but displayed different ranges in their predictions. In these different ranges, sensitivities were well-preserved in three of the four models. Conclusion Analyses of the effects of parameter variations in models can provide a comparative tool for the evaluation of model predictions. In addition, it can assist in uncovering model weak points and, in the case of disease models, be used to identify possible points for therapeutic intervention.

scholarly journals Reduced order modeling and analysis of the human complement system

2016 ◽  
Author(s):  
Adithya Sagar ◽  
Wei Dai ◽  
Mason Minot ◽  
Rachel LeCover ◽  
Jeffrey D. Varner
Keyword(s):  
Complement Activation ◽  
Therapeutic Intervention ◽  
Robustness Analysis ◽  
Model Parameters ◽  
Modeling And Analysis ◽  
Reduced Order ◽  
Alternative Pathways ◽  
Order Of Magnitude ◽  
Model Training

AbstractComplement is an important pathway in innate immunity, inflammation, and many disease processes. However, despite its importance, there are few validated mathematical models of complement activation. In this study, we developed an ensemble of experimentally validated reduced order complement models. We combined ordinary differential equations with logical rules to produce a compact yet predictive model of complement activation. The model, which described the lectin and alternative pathways, was an order of magnitude smaller than comparable models in the literature. We estimated an ensemble of model parameters fromin vitrodynamic measurements of the C3a and C5a complement proteins. Subsequently, we validated the model on unseen C3a and C5a measurements not used for model training. Despite its small size, the model was surprisingly predictive. Global sensitivity and robustness analysis suggested complement was robust to any single therapeutic intervention. Only the simultaneous knockdown of both C3 and C5 consistently reduced C3a and C5a formation from all pathways. Taken together, we developed a validated mathematical model of complement activation that was computationally inexpensive, and could easily be incorporated into pre-existing or new pharmacokinetic models of immune system function. The model described experimental data, and predicted the need for multiple points of therapeutic intervention to fully disrupt complement activation.

scholarly journals A new Bayesian methodology for nonlinear model calibration in Computational Systems Biology

2019 ◽  
Author(s):  
Fortunato Bianconi ◽  
Lorenzo Tomassoni ◽  
Chiara Antonini ◽  
Paolo Valigi
Keyword(s):  
Systems Biology ◽  
Nonlinear Model ◽  
Model Calibration ◽  
Sequential Monte Carlo ◽  
Profile Likelihood ◽  
Computational Cost ◽  
Robustness Analysis ◽  
Model Parameters ◽  
Computational Systems Biology ◽  
Proposal Distribution

AbstractComputational modeling is a common tool to quantitatively describe biological processes. However, most model parameters are usually unknown because they cannot be directly measured. Therefore, a key issue in Systems Biology is model calibration, i.e. estimate parameters from experimental data. Existing methodologies for parameter estimation are divided in two classes: frequentist and Bayesian methods. The first ones optimize a cost function while the second ones estimate the parameter posterior distribution through different sampling techniques. Here, we present an innovative Bayesian method, called Conditional Robust Calibration (CRC), for nonlinear model calibration and robustness analysis using omics data. CRC is an iterative algorithm based on the sampling of a proposal distribution and on the definition of multiple objective functions, one for each observable. CRC estimates the probability density function (pdf) of parameters conditioned to the experimental measures and it performs a robustness analysis, quantifying how much each parameter influences the observables behavior. We apply CRC to three Ordinary Differential Equations (ODE) models to test its performances compared to the other state of the art approaches, namely Profile Likelihood (PL), Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) and Delayed Rejection Adaptive Metropolis (DRAM). Compared with these methods, CRC finds a robust solution with a reduced computational cost. CRC is developed as a set of Matlab functions (version R2018), whose fundamental source code is freely available at https://github.com/fortunatobianconi/CRC.

Sensitivity and Robustness Analysis for Stochastic Model of Nanog Gene Regulatory Network

2015 ◽  
Vol 25 (07) ◽  
pp. 1540009 ◽  
Author(s):  
Qianqian Wu ◽  
Feng Jiang ◽  
Tianhai Tian
Keyword(s):  
Stochastic Model ◽  
Dynamic Property ◽  
Gene Network ◽  
Robustness Analysis ◽  
Network Models ◽  
Intrinsic Noise ◽  
Model Parameters ◽  
Extrinsic Noise ◽  
Nanog Expression ◽  
Nanog Gene

The advances of systems biology have raised a large number of mathematical models for exploring the dynamic property of biological systems. A challenging issue in mathematical modeling is how to study the influence of parameter variation on system property. Robustness and sensitivity are two major measurements to describe the dynamic property of a system against the variation of model parameters. For stochastic models of discrete chemical reaction systems, although these two properties have been studied separately, no work has been done so far to investigate these two properties together. In this work, we propose an integrated framework to study these two properties for a biological system simultaneously. We also consider a stochastic model with intrinsic noise for the Nanog gene network based on a published model that studies extrinsic noise only. For the stochastic model of Nanog gene network, we identify key coefficients that have more influence on the network dynamics than the others through sensitivity analysis. In addition, robustness analysis suggests that the model parameters can be classified into four types regarding the bistability property of Nanog expression levels. Numerical results suggest that the proposed framework is an efficient approach to study the sensitivity and robustness properties of biological network models.

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