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 in | International journal of industrial ergonomics Vol. 15; no. 1; pp. 39 - 47 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
1995
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Subjects | |
Online Access | Get full text |
ISSN | 0169-8141 1872-8219 |
DOI | 10.1016/0169-8141(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. |
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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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Keywords | Sensation of temperature Evaluation Regression analysis Fuzzy theory |
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References | Watada (BIB4) 1983; 11 Yanagishima (BIB5) 1988; 24 (BIB2) 1988 Tanaka (BIB3) 1990; 2 Okuno (BIB1) 1986 (10.1016/0169-8141(95)91249-A_BIB2) 1988 Tanaka (10.1016/0169-8141(95)91249-A_BIB3) 1990; 2 Watada (10.1016/0169-8141(95)91249-A_BIB4) 1983; 11 Okuno (10.1016/0169-8141(95)91249-A_BIB1) 1986 Yanagishima (10.1016/0169-8141(95)91249-A_BIB5) 1988; 24 |
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Title | A fuzzy logic analysis method for evaluating human sensitivities |
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