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Intelligent Techniques: An Overview
Published in Chiranji Lal Chowdhary, Intelligent Systems, 2019
On the other hand, fuzzy set claims that membership is concerned about degree of belongingness and it lies within the range of 0 and 1. Fuzzy logic is capable of portraying, to a reasonable extent, human type reasoning in natural form. Modeling of imprecise and qualitative knowledge, as well as handling of uncertainty is possible through a fuzzy system (Pedrycz, 1998). The working of a fuzzy system is represented in Figure 1.4. Fuzzy set have been used in many application domains to quantify the uncertainty for decision-making tasks (Iliadis, 2005). In addition to this, fuzzy sets are found to be effective in handling multi-objective decision-making processes. Fuzzy-logic-based decision-making approaches are being applied for risk assessment in the course of new product development (NPD) phase to determine the appropriate decision models and techniques (Wang, 1997; Buyukozkan and Feyzıoglu, 2004). Besides, fuzzy logic is also used in exploring knowledge from a database (Yager, 1996).
Soft Computing
Published in Vivek Kale, Digital Transformation of Enterprise Architecture, 2019
Fuzzy sets provide a natural framework for the process in dealing with uncertainty or imprecise data. Generally, they are suitable for handling the issues related to understand-ability of patterns, incomplete and noisy data, and mixed media information and human interaction and can provide approximate solutions faster. ANNs are nonparametric and robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms (GAs) provide efficient search algorithms to optimally select a model, from mixed media data, based on some preference criterion or objective function. Rough sets are suitable for handling different types of uncertainty in data. Neural networks and rough sets are widely used for classification and rule generation. Application of wavelet-based signal processing techniques is new in the area of soft computing. Wavelet transformation of a signal results in decomposition of the original signal in different multiresolution subbands. This is useful in dealing with compression and retrieval of data, particularly images. Other approaches like case-based reasoning and decision trees are also widely used to solve data mining problems.
Multi-objective Analysis
Published in Slobodan P. Simonović, Managing Water Resources, 2012
Quantitative criteria present some slightly different properties from qualitative criteria. It can be assumed that quantitative criteria are measured in some way, either directly or through calculation based on some model. They have stochastic properties which describe the probability of occurrence for values, based on future uncertainties for example. They also have some degree of imprecision in their measurement or modelling. In this way, quantitative criteria may have both stochastic and fuzzy properties. To prevent the complication of many decision-making problems, various uncertainties may be adequately represented with fuzzy sets. In general, the application of quantitative criteria within a fuzzy approach may assume that quantitative criteria are less fuzzy than qualitative criteria.
Urban restaurants and online food delivery during the COVID-19 pandemic: a spatial and socio-demographic analysis
Published in International Journal of Digital Earth, 2023
Bakhtiar Feizizadeh, Davoud Omrazadeh, Mohammad Ghasemi, Samaneh Bageri, Tobia Lakes, Robert Kitzmann, Abolfazl Ghanbari, Thomas Blaschke
In this study, we first determined the relevant indicators based on literature (Habibpour, Feiizadeh, and Jabarzadeh 2021), data availability, and the opinions of experts from the Municipality of Tabriz and the urban planning and marketing departments of the University of Tabriz. Then, we obtained the data from the Municipality of Tabriz, food order applications, the traffic police of Tabriz, and associated organizations (see Table 1). The required geometric and topological editing was then carried out in GIS, and the relevant indicators were derived. The criteria were standardized based on the nature of the pairwise comparison methodology, which is commonly used for rating and standardizing ordinal values. The standardization procedure was also used to prepare all data at the same scale suited for criterion weighting, sensitivity analysis and aggregation objectives. This was done using fuzzy standardization functions. A fuzzy set is essentially a set whose members can have membership degrees ranging from 0 to 1, as opposed to a traditional binary set, where each element must have a membership degree of either 0 or 1 (Malczewski 2006; Feizizadeh and Blaschke 2014). Figure 3 represent the spatial distribution and standardized version of the selected criteria.
A fuzzy best–worst method (BWM) to assess the potential environmental impacts of the process of ship recycling
Published in Maritime Policy & Management, 2022
Omer Soner, Erkan Celik, Emre Akyuz
Zadeh (1965) developed the fuzzy set theory to overcome the issues which occur in reality, in uncertain environments. A fuzzy set can be defined as , where is considered a set, and is the membership function, represented by . In relation to , in the discourse universe, each element is illustrated to a crisp interval number (Guo and Zhao 2017). Definition 1. Consider the is a fuzzy number when: , where .If becomes a closed interval.
Some Properties of Rough Pythagorean Fuzzy Sets
Published in Fuzzy Information and Engineering, 2021
Amal Kumar Adak, Davood Darvishi Salookolaei
Considering the imprecision in decision-making, Zadeh [1] introduced the idea of fuzzy set which has a membership function, that assigns to each element of the universe of discourse, a number from the unit interval to indicate the degree of belongingness to the set under consideration. The notion of fuzzy sets generalises classical sets theory, by allowing intermediate situations between the whole and nothing. In a fuzzy set, a membership function is defined to describe the degree of membership of an element to a class. The membership value ranges from to , where shows that the element does not belong to a class, means the element belongs to a class, and other values indicate the degree of membership to a class. For fuzzy sets, the membership function replaced the characteristic function in crisp sets. Nasseri et al. [2–5] conducted a lot of research work in the field of fuzzy set theory and they also used some interesting methods for ranking triangular fuzzy numbers and trapezoidal fuzzy numbers.