A fuzzy logic analysis method for evaluating human sensitivities

As a basic research tool for modeling human sensitivity, we have developed a program for analyzing sensitivity evaluation data using a fuzzy regression method. This takes into account the ambiguities and nonlinearity of human sensations and thus provides a method for quantifying their correlations w...

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Published inInternational journal of industrial ergonomics Vol. 15; no. 1; pp. 39 - 47
Main Authors Shimizu, Youji, Jindo, Tomio
Format Journal Article
LanguageEnglish
Published Elsevier B.V 1995
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ISSN0169-8141
1872-8219
DOI10.1016/0169-8141(95)91249-A

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Abstract As a basic research tool for modeling human sensitivity, we have developed a program for analyzing sensitivity evaluation data using a fuzzy regression method. This takes into account the ambiguities and nonlinearity of human sensations and thus provides a method for quantifying their correlations with physical characteristics. This was applied to a study of thermal sensation in a vehicle interior. Optimum membership functions were obtained that show the nonlinear relationship between each physical variable (e.g., air flow velocity, temperature and sun load) and thermal sensation. When a fuzzy regression analysis was performed using the membership functions, it was found that 85% of the evaluation scores that human subjects gave to various thermal sensations were included within the range of the estimated values. Relevance to industry We have been conducting basic studies aimed at the creation of comfortable vehicle interiors. These studies have focused on the development of interiors matching human sensitivities by quantifying the correlations between human sensations and the physical characteristics of the interior which influence them using fuzzy logic analysis method.
AbstractList As a basic research tool for modeling human sensitivity, we have developed a program for analyzing sensitivity evaluation data using a fuzzy regression method. This takes into account the ambiguities and nonlinearity of human sensations and thus provides a method for quantifying their correlations with physical characteristics. This was applied to a study of thermal sensation in a vehicle interior. Optimum membership functions were obtained that show the nonlinear relationship between each physical variable (e.g., air flow velocity, temperature and sun load) and thermal sensation. When a fuzzy regression analysis was performed using the membership functions, it was found that 85% of the evaluation scores that human subjects gave to various thermal sensations were included within the range of the estimated values.
As a basic research tool for modeling human sensitivity, we have developed a program for analyzing sensitivity evaluation data using a fuzzy regression method. This takes into account the ambiguities and nonlinearity of human sensations and thus provides a method for quantifying their correlations with physical characteristics. This was applied to a study of thermal sensation in a vehicle interior. Optimum membership functions were obtained that show the nonlinear relationship between each physical variable (e.g., air flow velocity, temperature and sun load) and thermal sensation. When a fuzzy regression analysis was performed using the membership functions, it was found that 85% of the evaluation scores that human subjects gave to various thermal sensations were included within the range of the estimated values. Relevance to industry We have been conducting basic studies aimed at the creation of comfortable vehicle interiors. These studies have focused on the development of interiors matching human sensitivities by quantifying the correlations between human sensations and the physical characteristics of the interior which influence them using fuzzy logic analysis method.
Author Shimizu, Youji
Jindo, Tomio
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Issue 1
Keywords Sensation of temperature
Evaluation
Regression analysis
Fuzzy theory
Language English
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SubjectTerms Evaluation
Fuzzy theory
Regression analysis
Sensation of temperature
Title A fuzzy logic analysis method for evaluating human sensitivities
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