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

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... Mamdani method was used for the FIS, owing to its intuitive and widely accepted nature. For Defuzzification, the centroid method is used as it has faster convergence speed than other methods [17,27]. Membership functions used in the simulation were triangular and trapezoidal shaped. ...
Conference Paper
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In this paper, a quarter-car suspension system has been investigated for the International Organization for Standardization (ISO)-classified road profile with various control strategies. The vehicle suspension system provides ride comfort and handling by reducing the transfer of road disturbances or irregular road profile to the passenger and cargo materials. The suspension also retains the road and tire contact, stabilizing the vehicle’s movements. A combination of fuzzy logic and neural network, i.e., adaptive neuro-fuzzy inference system (ANFIS), is deployed as a control strategy to control the quarter-car semi-active suspension model. Quarter-car suspension models with a passive control and semi-active controller with different control strategies, viz., Skyhook, Fuzzy Logic (FLC), and ANFIS, are designed and modeled in MATLAB/ SIMULINK®. Numerical simulations were performed on developed quarter-car models for an ISO-classified road profile disturbance, and the performance was compared. With respect to the passive suspension system, there is better ride comfort performance (9.7%) with skyhook control, but a compromise in handling, while FLC achieves both ride comfort (4%) and handling (6.12%), reducing the trade-off between both performances. This suspension performance is better achieved by the ANFIS ride comfort (54.57%) and handling (20.57%) with respect to (wrt) the passive suspension system. The comparative implementation of the above control strategies concludes better suspension by the ANFIS of the vehicle to the ISO-classified road disturbance than those other control strategies.
... A simple case of two elements of evidence per rule, the conformable rules will be: Therefore, the max-min compositional inference rule is: After appling the fuzzy rule in the input, which is fuzzified then the output is defused. Max-criterion [30,32], center-of-gravity, and the mean of maxima [31,33] are three techniques used in defuzzification. The most widespread technique is the Center of Gravity method (COG) that utilized in actual applications. ...
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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 ...
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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 actions and that of communicating the results to those responsible for safety, namely the dam owner or reservoir operator. ...
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... B y µ have to be defuzzified through a defuzzification method (Amaya et al. 2009). ...
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Purpose: Contingency estimating and management are critical management functions necessary for successful delivery of construction projects. Considering its importance, academics and industry professionals proposed a wide range of methods for risk quantification and accordingly for contingency estimating1. Considerably less work was directed to contingency management including risk mitigate during a project. Generally, there are two types of risks; (i) known risks which can be identified, evaluated, planned and budgeted for and (ii) unknown risks which may occur. These risks require a cost and time contingency, even if they were not planned for, in order to mitigate their impact in an orderly manner. In this respect, the importance of contingency management is critical in view of increasing project complexity and difficulty of estimating and/or allocating sufficient contingencies to mitigate risks encountered during project execution. This paper focuses on the contingency management from two perspectives; estimation and depletion of contingency over project durations. Method: A new method is developed using fuzzy sets theory2 along with a set of measures and indices to model the uncertainty inherent in this process. This method includes a possibility measure, an agreement index, a fussiness measure, an ambiguity measure and a quality fuzzy number index. These measures and indices provide not only the possibility of having adequate contingency but also address issues of precision and vagueness associated with the uncertainty involved. The paper also presents a comparison between the commonly used Monte Carlo Simulation method and the proposed direct fuzzy-sets-based method. As to depletion, the paper presents a management procedure focusing on depletion of the contingency in a generic computational platform. The developed procedure makes use of policies and procedures3 followed by leading construction organizations and owners of major constructed facilities. The developed method and its computational platform were coded using VB.NET-programming. Results & Discussion: A numerical example is analysed to demonstrate the use of the developed method and to illustrate its capabilities beyond those of the traditional Monte Carlo Simulation.
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