Comparison of Defuzzification Methods: Automatic Control of Temperature and Flow inHeat Exchanger

Amaya, Alvaro J. Rey; Lengerke, Omar; Cosenza, Carlos Alberto Nunes; Dutra, Max Suell; Tavera, Magda Judith Morales

doi:10.5772/7899

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…The commonly used method for defuzzification is the center of area method (COA) which can be expressed as (Amaya et al 2009): * = ∫ ( ) ( ) Equation ( 5)…”

Section: Defuzzificationmentioning

confidence: 99%

Risk quantification using fuzzy-based Monte Carlo simulation

Moselhi¹,

Roghabadi²

2020

ITcon

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Estimating cost contingency of construction projects depends largely on data captured from previous projects and/or experience and judgment of members of project team. Mote Carlo simulation is commonly used in estimating contingency, where its accuracy was reported to depend on number of iterations used in the simulation process, probability density functions associated with each project cost item being considered and the correlation among these cost items. The literature reveals that the latter is the most important issue for accurate estimate of contingency. It, however, requires the calculation of coefficients of correlation among cost items based on captured historical records of cost data. Subjective correlation was introduced to alleviate the difficulties associated with the calculation of these coefficients. This paper presents a newly developed method for cost contingency estimation that considers subjective correlations and allows for contingency estimation with and without computer simulation. Unlike the methods reported in the literature, the present method considers uncertainty associated with the coefficients of correlation and utilizes earlier work of the first author in calculating the variance of total project cost. It also allows for assessing the impact of variable covariance matrix on the estimated project cost using a simple and user-friendly computational platform. The application of the developed method on cost data captured from two databases demonstrates its use and accuracy in estimating cost contingency. The results are compared to those produced by others using Monte Carlo Simulation with and without correlation using an actual project data.

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“…The commonly used method for defuzzification is the center of area method (COA) which can be expressed as (Amaya et al 2009): * = ∫ ( ) ( ) Equation ( 5)…”

Section: Defuzzificationmentioning

confidence: 99%

Risk quantification using fuzzy-based Monte Carlo simulation

Moselhi¹,

Roghabadi²

2020

ITcon

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“…where (m 1 ), (m 2 ), (m 3 ) are the lower bound, the strongest membership degree, and the upper bound of (M), respectively; (W i ) is the importance weight of the attribute (i) calculated by the ANP. The fuzzy number of the overall risk maturity is transferred to a crisp number employing the center of the area method, which is a commonly used method for defuzzification [25]. The expected value (EV) represents the defuzzified value of a fuzzy number according to Equation (4) [26]:…”

Section: Fuzzy Set Theorymentioning

confidence: 99%

A Fuzzy-Based Decision Support Model for Risk Maturity Evaluation of Construction Organizations

Roghabadi

Moselhi

2020

Algorithms

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Risk maturity evaluation is an efficient tool which can assist construction organizations in the identification of their strengths and weaknesses in risk management processes and in taking necessary actions for the improvement of these processes. The accuracy of its results relies heavily on the quality of responses provided by participants specialized in these processes across the organization. Risk maturity models reported in the literature gave equal importance to participants’ responses during the model development, neglecting their level of authority in the organization as well as their level of expertise in risk management processes. Unlike the existing models, this paper presents a new risk maturity model that considers the relative importance of the responses provided by the participants in the model development. It considered their authority in the organization and their level of involvement in the risk management processes for calculating the relative weights associated with the risk maturity attributes. It employed an analytic network process (ANP) to model the interdependencies among the risk maturity attributes and utilizes the fuzzy set theory to incorporate the uncertainty associated with the ambiguity of the responses used in the model development. The developed model allows the construction organizations to have a more accurate and realistic view of their current performance in risk management processes. The application of the developed model was investigated by measuring the risk maturity level of an industrial partner working on civil infrastructure projects in Canada.

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“…However, applications usually rely on standard methods, centroids or means of maxima (Runkler, 1997;Wang 2009; Rouhparvar and Panahi 2015) but very few works deal with the problem of choosing defuzzification methods. For example, an analysis of several defuzzification methods is proposed for the automatic control of temperature and flow in heat exchangers (Amaya et al, 2009), for route selection for public bus routing (Nurcahyo et al, 2003), and to assess the quality of fuzzy control of a nuclear reactor (Zeleznikow and Nolan, 2001). The question of defuzzification method is raised in the case of dam performance assessment with two intentions: that of proposing corrective Author-produced version of the article published in Expert Systems with Applications, 2017, 70, 160-174.…”

Section: Introductionmentioning

confidence: 99%

Selection of appropriate defuzzification methods: Application to the assessment of dam performance

Talon¹,

Curt²

2017

Expert Systems with Applications

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The performance assessment of dams is of major importance for optimizing maintenance management. A methodology is provided here to assess the risk of dam failure using data collected during in-situ inspections and from the design and follow-up files. This assessment takes into account uncertainty associated with the data and the assessment process. The result of this assessment is introduced in a fuzzy frame. This paper presents the approach taken to choose defuzzification methods that allow extracting the information necessary from this possibility distribution so that dam experts can both rank dam maintenance actions and communicate the results of this assessment. This paper first presents the process used to assess dam performance after which it presents the defuzzification methods available. A sensitivity analysis is then performed to select the methods most relevant for our case study. The last part presents the development of a tool useful for the application of defuzzification methods to our problematic and describes a real case study in which the selected defuzzification methods are used. HIGHLIGHTS We propose an approach for selecting appropriate defuzzification methods that fulfills the requirements of a group of experts. We propose a sensitivity analysis of defuzzification methods. We apply this approach to the assessment of dam performance. Methods were chosen in relation to the objectives (decision-support and communication) defined by a group of dam experts. The approach is applied to a real case.

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Comparison of Defuzzification Methods: Automatic Control of Temperature and Flow inHeat Exchanger

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Cited by 5 publications

References 4 publications

Risk quantification using fuzzy-based Monte Carlo simulation

Risk quantification using fuzzy-based Monte Carlo simulation

A Fuzzy-Based Decision Support Model for Risk Maturity Evaluation of Construction Organizations

Selection of appropriate defuzzification methods: Application to the assessment of dam performance

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