Mamdani fuzzy rule-based models for psychological research

Pandey, Deepak; Kushwaha, Govind Singh; Kumar, Sanjay

doi:10.1007/s42452-020-2726-z

Cited by 15 publications

(8 citation statements)

References 25 publications

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“…Simply put, in the light of FFISs, so many applications can be considered in which applying FFIS yields to more satisfactory results than ever before. The fields in which FFIS can prove applicable include fault detection [17], control systems [18], [19], handoff decision algorithms [20], internet of thing [21], risk assessment [22], [23], evaluating the quality of experience [24], decision support systems [25], fuzzy clustering and classification [26], [27], fuzzy image processing [28], fuel cell stack problem [29], educational systems [30], fuzzy modelling [31], psychology [32], emotion categories [33], packet scheduling algorithms [34], multiobjective optimization problem [35], decision-making [36], heuristic algorithms [37], etc.…”

Section: Discussionmentioning

confidence: 99%

Fractional Fuzzy Inference System: The New Generation of Fuzzy Inference Systems

Mazandarani

2020

IEEE Access

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This paper presents a new machinery of compositional rule of inference called fractional fuzzy inference system (FFIS). An FFIS is a fuzzy inference system (FIS) in which consequent parts of a rule base consist of a new type of membership functions called fractional membership functions. Fractional membership functions are characterized using fractional indices. There are two types of fractional indices. Each type can be either constant or dynamic. An FFIS intelligently considers not only the truth degrees of information included in membership functions, but also the volume of the information in the process of making a conclusion. In other words, the volume of information extracted from a membership function depends on the truth degree of information. Concretely, the higher the truth degree, the larger the volume of information that is involved in the process of making a conclusion. It is shown that typical FISs, e.g. Mamdani's or Larsen's FISs, are special cases of FFISs. Specifically, as the fractional indices approach one, the FFIS approaches a typical FIS. In addition, using two theorems proved in this paper, it is demonstrated that, independent of the problem in question, a typical FIS never leads to results which are more satisfactory than those obtained by the FFIS corresponding to the typical FIS provided that a particular set of fractional indices is taken into account. Put another way, it seems sound to expect that applying FFIS always leads to more satisfactory results than applying its corresponding FIS. It is also shown that FFIS grants a special dynamic to FIS which can be also customized according to a new concept called reaction trajectories map (RTM). Particularly, the RTM enables decision makers to select an FFIS more suitable for their purpose. Some more concepts such as the left and right orders of an FFIS and the fracture index are also introduced in this paper.

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Section: Discussionmentioning

confidence: 99%

Fractional Fuzzy Inference System: The New Generation of Fuzzy Inference Systems

Mazandarani

2020

IEEE Access

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“…MFRBS has been extensively employed to benefit from its simple logic approach, and easy graphical illustration [ 48 ]. The reason behind implementing MFRBS is that MFRBS is the most used fuzzy rule-based model due to its inherent characteristic of handling the nonlinear association between inputs and output [ 49 ]. Moreover, the Mamdani inference system is well suited to human input in comparison to the Sugeno inference system which is well suited to mathematical analysis.…”

Section: Dematel Approachmentioning

confidence: 99%

“…In terms of MFRBS, overall the advantage of implementing this method is that the output of each rule is a fuzzy set. While Mamdani systems have further intuitive and simpler-to-classify rule bases, they are well-suited to expert system applications where the rules are designed from human expertise and designed based on human decisions, for instance, medical diagnostics [ 49 ]. Moreover, the systems have an easy structure and can be made simply.…”

Section: Dematel Approachmentioning

confidence: 99%

A Hybrid MCDM Approach Based on Fuzzy-Logic and DEMATEL to Evaluate Adult Obesity

Safaei

Sundararajan

Asadi

et al. 2022

IJERPH

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Obesity and its complications is one of the main issues in today’s world and is increasing rapidly. A wide range of non-contagious diseases, for instance, diabetes type 2, cardiovascular, high blood pressure and stroke, numerous types of cancer, and mental health issues are formed following obesity. According to the WHO, Malaysia is the sixth Asian country with an adult population suffering from obesity. Therefore, identifying risk factors associated with obesity among Malaysian adults is necessary. For this purpose, this study strives to investigate and assess the risk factors related to obesity and overweight in this country. A quantitative approach was employed by surveying 26 healthcare professionals by questionnaire. Collected data were analyzed with the DEMATEL and Fuzzy Rule-Based methods. We found that lack of physical activity, insufficient sleep, unhealthy diet, genetics, and perceived stress were the most significant risk factors for obesity.

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“…The fuzzy system classifies complex decision-making difficulties into an easy hierarchical structure and then conducts evaluations by pair-wise comparisons. Fuzzy logic enables researchers to examine the input data imprecision and vague it comprehensively, and generate a more consistent model for calculating input-output relations [77].…”

Section: Mamdani Fuzzy Rule-based Systemsmentioning

confidence: 99%

Evaluation of Factors to Respond to the COVID-19 Pandemic Using DEMATEL and Fuzzy Rule-Based Techniques

Asadi

Nilashi

Abumalloh

et al. 2021

Int. J. Fuzzy Syst.

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Since social and environmental conditions have changed dramatically in recent years, the spectrum of diseases caused by infections is also evolving rapidly. The outspread of COVID-19 has resulted in an emergency situation across the globe with significant effects on the population's lives, families, and societies, leading to concerns the World Health Organization. Accordingly, the virus has substantially threatened the Malaysians' public health and contributed considerably to increased healthcare expenses. Since the novel coronavirus was found in China, Malaysia's government has started its actions according to the World Health Organization procedures and concentrated on addressing and preventing the spread of the infection. The present paper aims to find and evaluate the factors to respond to the COVID-19 outbreak in Malaysia, limiting the outspread of the disease in this country. This study used the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Fuzzy Rule-Based techniques to evaluate the factors through a set of questionnaires completed by the health care professionals. According to the data analysis results, movement control order, international travel restrictions, and the mass gathering cancellations were of most importance in the prevention of COVID-19 infections transmission.

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Mamdani fuzzy rule-based models for psychological research

Cited by 15 publications

References 25 publications

Fractional Fuzzy Inference System: The New Generation of Fuzzy Inference Systems

Fractional Fuzzy Inference System: The New Generation of Fuzzy Inference Systems

A Hybrid MCDM Approach Based on Fuzzy-Logic and DEMATEL to Evaluate Adult Obesity

Evaluation of Factors to Respond to the COVID-19 Pandemic Using DEMATEL and Fuzzy Rule-Based Techniques

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