New approach to sensitivity analysis of multiple equilibria in solutions

1994 ◽  
Vol 90 (21) ◽  
pp. 3245-3252 ◽  
Author(s):  
Ilie Fishtik ◽  
István Nagypál ◽  
Ivan Gutman
Keyword(s):  
Sensitivity Analysis ◽  
Multiple Equilibria ◽  
New Approach

scholarly journals DISSECTING GALAXY FORMATION MODELS WITH SENSITIVITY ANALYSIS—A NEW APPROACH TO CONSTRAIN THE MILKY WAY FORMATION HISTORY

2014 ◽  
Vol 787 (1) ◽  
pp. 20 ◽  
Author(s):  
Facundo A. Gómez ◽  
Christopher E. Coleman-Smith ◽  
Brian W. O'Shea ◽  
Jason Tumlinson ◽  
Robert L. Wolpert
Keyword(s):  
Sensitivity Analysis ◽  
Galaxy Formation ◽  
Milky Way ◽  
New Approach ◽  
Formation History

scholarly journals A New Approach for Sampling Ordered Parameters in Probabilistic Sensitivity Analysis

2017 ◽  
Vol 36 (3) ◽  
pp. 341-347 ◽  
Author(s):  
Shijie Ren ◽  
Jonathan Minton ◽  
Sophie Whyte ◽  
Nicholas R. Latimer ◽  
Matt Stevenson
Keyword(s):  
Sensitivity Analysis ◽  
Probabilistic Sensitivity Analysis ◽  
New Approach

Design Improvement by Sensitivity Analysis (DISA) Under Interval Uncertainty Using Multi-Objective Optimization

2009 ◽  
Author(s):  
J. Hamel ◽  
M. Li ◽  
S. Azarm
Keyword(s):  
Sensitivity Analysis ◽  
Interval Uncertainty ◽  
Minimal Amount ◽  
Engineering System ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Design Improvement ◽  
Multi Objective ◽  
System A

Uncertainty in the input parameters to an engineering system may not only degrade the system’s performance, but may also cause failure or infeasibility. This paper presents a new sensitivity analysis based approach called Design Improvement by Sensitivity Analysis (DISA). DISA analyzes the interval parameter uncertainty of a system and, using multi-objective optimization, determines an optimal combination of design improvements required to enhance performance and ensure feasibility. This is accomplished by providing a designer with options for both uncertainty reduction and, more importantly, slight design adjustments. The approach can provide improvements to a design of interest that will ensure a minimal amount of variation in the objective functions of the system while also ensuring the engineering feasibility of the system. A two stage sequential framework is used in order to effectively employ metamodeling techniques to approximate the analysis function of an engineering system and greatly increase the computational efficiency of the approach. This new approach has been applied to two engineering examples of varying difficulty to demonstrate its applicability and effectiveness.

Methodology for Risk Assessment and Costs Associated with Risk Occurrence in E-Government Projects

2015 ◽  
pp. 349-369 ◽  
Author(s):  
Neven Vrček ◽  
Petra Peharda ◽  
Dušan Munđar
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Sensitivity Analysis ◽  
Bayesian Networks ◽  
Project Managers ◽  
New Approach ◽  
Management Quality ◽  
Government Project ◽  
Project Risks ◽  
Graphical Structure

The main purpose of this chapter is to emphasize the problem of e-government project risks and to introduce a methodology for risk assessment and calculation of costs associated with risk occurrence in e-government projects based on Bayesian networks. The proposed methodology presents a new approach to the assessment of risks and costs related to e-government project risks. As such, it facilitates the holistic decision making procedure for project managers. The application of Bayesian networks in the context of risks and risk related costs reduces the level of uncertainty in e-government projects and provides a graphical structure of risks and corresponding costs. Finally, the sensitivity analysis has also been integrated into the methodology and its results can have a significant impact on the overall project management quality.

Methodology for Risk Assessment and Costs Associated with Risk Occurrence in E-Government Projects

2015 ◽  
pp. 449-469
Author(s):  
Neven Vrček ◽  
Petra Peharda ◽  
Dušan Munđar
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Sensitivity Analysis ◽  
Bayesian Networks ◽  
Project Managers ◽  
New Approach ◽  
Management Quality ◽  
Government Project ◽  
Project Risks ◽  
Graphical Structure

The main purpose of this chapter is to emphasize the problem of e-government project risks and to introduce a methodology for risk assessment and calculation of costs associated with risk occurrence in e-government projects based on Bayesian networks. The proposed methodology presents a new approach to the assessment of risks and costs related to e-government project risks. As such, it facilitates the holistic decision making procedure for project managers. The application of Bayesian networks in the context of risks and risk related costs reduces the level of uncertainty in e-government projects and provides a graphical structure of risks and corresponding costs. Finally, the sensitivity analysis has also been integrated into the methodology and its results can have a significant impact on the overall project management quality.

scholarly journals ANFIS modeling and sensitivity analysis for estimating solar still productivity using measured operational and meteorological parameters

2017 ◽  
Vol 18 (4) ◽  
pp. 1437-1448 ◽  
Author(s):  
Ahmed F. Mashaly ◽  
A. A. Alazba
Keyword(s):  
Sensitivity Analysis ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Membership Functions ◽  
Solar Still ◽  
Inference System ◽  
New Approach ◽  
Neuro Fuzzy ◽  
Input Parameters ◽  
Statistical Criteria

Abstract This study investigates a potential application of the adaptive neuro-fuzzy inference system (ANFIS) as a relatively new approach for predicting solar still productivity (SSP). Five variables, relative humidity (RH), solar radiation (SR), feed flow rate (MF), and total dissolved solids of feed (TDSF) and brine (TDSB), were used as input parameters. The data were collected from an experimental solar still system used to desalinate seawater in an arid climate. The data were distributed randomly into training, testing, and validation datasets. A hybrid learning algorithm and eight different membership functions were applied to generate the ANFIS models. Several statistical criteria were used to assess the model performances. The ANFIS model with a generalized bell membership function provided the best prediction accuracy compared with models with other membership functions. The coefficient of correlation values for this model were 0.999, 0.959, and 0.832 for training, testing, and validation datasets, respectively. Sensitivity analysis (SA) was used to show the effectiveness of the considered input parameters for predicting SSP. The SA results indicated that SSP is the most influential parameter on SSP. Generally, the findings indicate the robustness of the ANFIS approach for estimating SSP.

Design Improvement by Sensitivity Analysis Under Interval Uncertainty Using Multi-Objective Optimization

2010 ◽  
Vol 132 (8) ◽  
Author(s):  
J. Hamel ◽  
M. Li ◽  
S. Azarm
Keyword(s):  
Sensitivity Analysis ◽  
Degrees Of Freedom ◽  
Interval Uncertainty ◽  
Engineering System ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Design Improvement ◽  
Multi Objective ◽  
Original Analysis ◽  
Input Parameters

Uncertainty in the input parameters to an engineering system may not only degrade the system’s performance but may also cause failure or infeasibility. This paper presents a new sensitivity analysis based approach called design improvement by sensitivity analysis (DISA). DISA analyzes the interval uncertainty of input parameters and using multi-objective optimization, determines an optimal combination of design improvements that will ensure a minimal variation in the objective functions of the system, while also ensuring the feasibility. The approach provides a designer with options for both uncertainty reduction and, more importantly, slight design adjustments. A two-stage sequential framework is used that can employ either the original analysis functions or their metamodels to greatly increase the computational efficiency of the approach. This new approach has been applied to two engineering examples of varying difficulty to demonstrate its applicability and effectiveness. The results produced by these examples show the ability of the approach to ensure the feasibility of a preexisting design under interval uncertainty by effectively adjusting available degrees of freedom in the system without the need to completely redesign the system.

scholarly journals Weighting-Based Sensitivity Analysis in Causal Mediation Studies

2018 ◽  
Vol 43 (1) ◽  
pp. 32-56 ◽  
Author(s):  
Guanglei Hong ◽  
Xu Qin ◽  
Fan Yang
Keyword(s):  
Sensitivity Analysis ◽  
Functional Form ◽  
Potential Bias ◽  
Initial Weight ◽  
Measurement Scales ◽  
New Approach ◽  
Causal Mediation ◽  
Outcome Relationship ◽  
New Strategy

Through a sensitivity analysis, the analyst attempts to determine whether a conclusion of causal inference could be easily reversed by a plausible violation of an identification assumption. Analytic conclusions that are harder to alter by such a violation are expected to add a higher value to scientific knowledge about causality. This article presents a weighting-based approach to sensitivity analysis for causal mediation studies. Extending the ratio-of-mediator-probability weighting (RMPW) method for identifying natural indirect effect and natural direct effect, the new strategy assesses potential bias in the presence of omitted pretreatment or posttreatment covariates. Such omissions may undermine the causal validity of analytic conclusions. The weighting approach to sensitivity analysis reduces the reliance on functional form assumptions and removes constraints on the measurement scales for the mediator, the outcome, and the omitted covariates. In its essence, the discrepancy between a new weight that adjusts for an omitted confounder and an initial weight that omits the confounder captures the role of the confounder that contributes to the bias. The effect size of the bias due to omitted confounding of the mediator–outcome relationship is a product of two sensitivity parameters, one associated with the degree to which the omitted confounders predict the mediator and the other associated with the degree to which they predict the outcome. The article provides an application example and concludes with a discussion of broad applications of this new approach to sensitivity analysis. Online Supplemental Material includes R code for implementing the proposed sensitivity analysis procedure.

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