The fuzzy modelling relation and its application in psychology and artificial intelligence

Averkin, Alexey; Tarasov, V. B.

doi:10.1016/0165-0114(87)90003-0

Cited by 21 publications

(14 citation statements)

References 5 publications

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“…The applications of fuzzy logic in psychology researches have been started since mid-1980 [ 19 ]. Averkin and Tarasov in [ 20 ] examined application of fuzzy modeling relation in psychology. Hesketh et al considered [ 21 ] application of fuzzy graphical rating scale to the psychology.…”

Section: Application Of Fuzzy Logic and Z -Numbmentioning

confidence: 99%

Application ofZ-Number Based Modeling in Psychological Research

Aliev

Memmedova

2015

Computational Intelligence and Neuroscience

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Pilates exercises have been shown beneficial impact on physical, physiological, and mental characteristics of human beings. In this paper, Z-number based fuzzy approach is applied for modeling the effect of Pilates exercises on motivation, attention, anxiety, and educational achievement. The measuring of psychological parameters is performed using internationally recognized instruments: Academic Motivation Scale (AMS), Test of Attention (D2 Test), and Spielberger's Anxiety Test completed by students. The GPA of students was used as the measure of educational achievement. Application of Z-information modeling allows us to increase precision and reliability of data processing results in the presence of uncertainty of input data created from completed questionnaires. The basic steps of Z-number based modeling with numerical solutions are presented.

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Section: Application Of Fuzzy Logic and Z -Numbmentioning

confidence: 99%

Application ofZ-Number Based Modeling in Psychological Research

Aliev

Memmedova

2015

Computational Intelligence and Neuroscience

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“…Zadeh [33] gave the notion of fuzzy set to handle the uncertainty which is caused by imprecise information and vague data. The interest of psychologist in fuzzy logic has visibly been growing since mid-1980s [1,11,25,26]. Psychology is not only a field in which profound applications of fuzzy logic is anticipated, but is also very important for the development of fuzzy set theory itself [34].…”

Section: Introductionmentioning

confidence: 99%

Mamdani fuzzy rule-based models for psychological research

2020

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The biasness of the participants in psychological research cannot be ignored during answering various psychological questioners or inventory. Hence, the prediction of psychological parameters can be deemed an ambiguous endeavor and fuzzy modeling provides a mean to account for this ambiguity and uncertainty. In the present study, two fuzzy rulebased models that use single input and generate single output are developed to convert the raw scores of neuroticism and extraversion to standard scores. Maudsley personality inventory (MPI) and Sinha's comprehensive anxiety test (SCAT) were used to collect raw data of neuroticism, extraversion and anxiety from participants. Using the standard scores for neuroticism and extroversion, third fuzzy rule-based model is also developed to predict the anxiety level of the participants. Each model is a collection of fuzzy rules that express the relationship of each input to the output. The performance of all developed models is tested by estimating mean absolute percentage error (MAPE) and paired two-tailed t test.

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“…Similarly, each value of distance metric scale, for example, the distance 5m from robot to object can correspond not for one but for several terms (for example, "not far from" with degree 0,5 and "closely" with degree 0,1). In [7] such fuzzy correspondence between set of environmental states and the set of its internal model states was named as fuzzy modeling relation. Its fuzziness shows fundamental constraint on model precision: in view of environmental complexity and limited robot's sensors capacity it is impossible to construct homomorphic correspondence between environmental states and its model states in robot.…”

Section: Introductionmentioning

confidence: 99%

Constructing robot's model of external environment on basis of linguistic relations and generalized constraints

Kalutskaya

2009

2009 Fifth International Conference on Soft Computing, Computing With Words and Perceptions in System Analysis, Decision and Co

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The problem of constructing internal model of external environment for intelligent robot is considered. Basic types of relations are discussed to specify robot's situational behavior.A new approach to linguistic internal model specification based on the synthesis of Pospelov's logical-linguistic modeling and Zadeh's generalized constraints is suggested. A short presentation of generalized constraints types and techniques is given. The expression of space and time relations in the form of generalized constraints is proposed; some relevant examples are shown.

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The fuzzy modelling relation and its application in psychology and artificial intelligence

Cited by 21 publications

References 5 publications

Application ofZ-Number Based Modeling in Psychological Research

Application ofZ-Number Based Modeling in Psychological Research

Mamdani fuzzy rule-based models for psychological research

Constructing robot's model of external environment on basis of linguistic relations and generalized constraints

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