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About 2,670 papers for “defuzzification”

Defuzzification

Enric Trillas, Philippe Smets, Enrique H. Ruspini et al. · 1998

Cited by 0

Defuzzification

M. Mizumoto · 2020

The conventional fuzzy inference model consists of three basic steps: fuzzification, inference process, and defuzzification. Fuzzification is a mapping from the observed input to the fuzzy sets defined in the corresponding universe of discourse. The inference process is a decision-making logic which determines fuzzy outputs corresponding to fuzzified or crisp inputs, with respect to the fuzzy rules. Defuzzification produces a nonfuzzy output that best represents the inferred fuzzy output. Defuzzification problems emerge from the application of fuzzy control to industrial processes. The output of fuzzy controllers must be in a defuzzified form, because mechanical, electrical, phonetic, and other actuators can only accept and use deterministic signals. In existing fuzzy logic controllers, the center of gravity method is widely used as a defuzzification method which decides an actual control action from a fuzzy set of control actions inferred from fuzzy control rules. A number of defuzzification methods leading to distinct results have been proposed in the literature. We shall introduce and discuss 11 defuzzification methods which have been predominant in the literature on fuzzy control and fuzzy decision making.

Cited by 3

DEFUZZIFICATION OF TIFNs

Iuliana Carmen · 2015

In the present paper we complete the above fuzzy assessment approach by presenting a defuzzification method of TIFNS based on the Center of Gravity (COG) technique, which enables the required comparison.

Cited by 0

Fuzzification and defuzzification.

Robert Küffner (209172), Tobias Petri (361639), Lukas Windhager (138964) et al. · Figshare · 2015

<p>We use triangular membership functions to fuzzify the continuous gene levels of an effector <i>e</i> into fuzzy sets. As shown by the magenta arrow, a continuous gene level of <i>l<sub>e</sub></i> = 0.25 is fuzzified into the fuzzy value <<i>L</i>(<i>low,l<sub>e</sub></i>) = 0.5, <i>L(med,l<sub>e</sub>)</i> = 0.5, <i>L</i>(<i>high,l<sub>e</sub></i>) = 0.0>. This can be reversed by defuzzification without loss of information.</p>

Cited by 0Open access

Optoelectronic Defuzzification Devices

RUS Ростовский государственный университет путей сообщения, Mike Alles, S. M. Kovalev et al. · Физические основы приборостроения · 2012

Cited by 0

Radial defuzzification method

Shounak Roychowdhury, B.H. Wang, Seung Kwon Ahn · 1994

Till now the problem of defuzzification has not been explained very specifically. Conceptually it is not as simple a problem as it looks. It is one of fundamental problems of selection and decision making. In this paper the authors attempt to understand the problem from the perspective of the neighbors. The neighbors interact and poll to reach a deterministic decision.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 5

Decision matrix post-defuzzification.

Amirreza Salehi (20496397), Ardavan Babaei (20496400), Majid Khedmati (14828240) · Figshare · 2025

&lt;div&gt;&lt;p&gt;Predicting incident duration and understanding incident types are essential in traffic management for resource optimization and disruption minimization. Precise predictions enable the efficient deployment of response teams and strategic traffic rerouting, leading to reduced congestion and enhanced safety. Furthermore, an in-depth understanding of incident types helps in implementing preventive measures and formulating strategies to alleviate their influence on road networks. In this paper, we present a comprehensive framework for accurately predicting incident duration, with a particular emphasis on the critical role of street conditions and locations as major incident triggers. To demonstrate the effectiveness of our framework, we performed an in-depth case study using a dataset from San Francisco. We introduce a novel feature called "Risk" derived from the Risk Priority Number (RPN) concept, highlighting the significance of the incident location in both incident occurrence and prediction. Additionally, we propose a refined incident categorization through fuzzy clustering methods, delineating a unique policy for identifying boundary clusters that necessitate further modeling and testing under varying scenarios. Each cluster undergoes a Multiple Criteria Decision-Making (MCDM) process to gain deeper insights into their distinctions and provide valuable managerial insights. Finally, we employ both traditional Machine Learning (ML) and Deep Learning (DL) models to perform classification and regression tasks. Specifically, incidents residing in boundary clusters are predicted utilizing the scenarios outlined in this study. Through a rigorous analysis of feature importance using top-performing predictive models, we identify the "Risk" factor as a critical determinant of incident duration. Moreover, variables such as distance, humidity, and hour demonstrate significant influence, further enhancing the predictive power of the proposed model.&lt;/p&gt;&lt;/div&gt;

Cited by 0Open access

Defuzzification Output related to .

Yanyan Dong (1304145) · Figshare · 2025

<div> The frequent development and unpredictable, dynamic nature of industrial enterprises require an effective financial efficiency detection process. The prediction process uses a large volume of information to identify the details of resources and operational performance in industrial applications. Traditional statistical techniques like regression analysis, decision tree, and machine learning approaches significantly improve prediction efficiency. However, the existing methods face uncertainty, robustness, and scalability issues when exploring high-dimensional data. The research difficulties are addressed by integrating the Fuzzy logic and mathematical model called the FuzzyMath approach. The FuzzyMath concept understands the industrial economic details and predicts financial performance with maximum recognition accuracy. The collected inputs are explored using fuzzy systems that use the multivariate and salp optimization algorithm at every step to improve the overall system efficiency. The optimized membership function, fuzzy rule, and defuzzification process minimize the computation difficulties and can handle the uncertainty issues effectively. Thus, the FuzzyMath-based created rules ensure 99.23% accuracy while predicting financial efficiency in industrial applications. </div>

Cited by 0Open access

Empirical study of defuzzification

S.S. Lancaster, Mark J. Wierman · 2004

The most important application of fuzzy logic is designing controllers. Fuzzy logic controllers (FLC) are much easier to design than non-linear controllers of similar capabilities. The rules that a designer needs to create are often based on their current experience and knowledge. Conventional FLCs use Center of Gravity or Mean of Maxima defuzzification methods, though other methods have been studied. This paper compares the efficiency of many different models of the defuzzification process. The goal is to examine the accuracy of the output data and the amount of processing time required. A simple controller that backs a truck up to a gate is used in the study. All of the variables are granulated with trapezoidal fuzzy numbers. Some of the defuzzification methods examined are Fast Center of Gravity, Mean of Maxima, True Center of Gravity and various new methods that have shown promise in application.

Cited by 18

Combinability functions in defuzzification

D. Swiatlowski, C. Isik · 2002

We overview various knowledge-based defuzzification functions from a system modeling and controls perspective, and introduce a new variant which is useful in applications where fuzzy variables are related to deviations from a norm, such as feedback control. We also summarize results of simulation experiments.

Cited by 1

Defuzzification in Fuzzy Controllers

Hans Hellendoorn, Christoph Thomas · Journal of Intelligent & Fuzzy Systems · 1993

An important subject in fuzzy control theory is tuning of a fuzzy controller. If one wants to tune a fuzzy controller, one can focus on the choice of rules, membership functions, number of input and output fuzzy sets and their degree of overlapping, implication, and connection operations, and defuzzification method. All these choices are closely related and in no way independent of each other. We describe six important defuzzification methods and their respective merits and shortcomings, dependent on the rules, domains, etc. Further, we give an alternative approach for the case in which the output fuzzy sets have different shapes or are asymmetrical. We illustrate this by several examples.

Cited by 295

Two new defuzzification techniques

Daniel Brubaker · Proceedings of WESCON '93 · 2002

The term defuzzification in a fuzzy rule-base system refers to the transforming of an action designated in fuzzy terms by the rule-base into a crisp value that can be executed. This paper discusses briefly the three currently most popular defuzzification techniques-the maximizer, centroid and singleton-with their strengths and shortcomings, and then introduces two new techniques, one quite general, and the other specific to relatively small, embedded systems.

Cited by 2

Defuzzification of fuzzy intervals

Renxiong Zhao, Rakesh Govind · [1990] Proceedings. First International Symposium on Uncertainty Modeling and Analysis · 2002

Fuzzy intervals are classified as regular or irregular based on the defuzzification method. A fuzzy interval is regular if its membership function is maximized at its defuzzified value. If the center-of-area method or the fuzzy-mean method is used, the regularity of a fuzzy interval is preserved after applying an arithmetic operation with nonzero real number. Further, the regularities based on the center-of-area method are preserved after addition and subtraction operations on two or more regular fuzzy intervals.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 3