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Textile Research Journal
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Predicting Clothing Sensory Comfort with Artificial Intelligence Hybrid Models

A.S.W. Wong

Institute of Textiles and Clothing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

Y. Li

Institute of Textiles and Clothing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

P.K.W. Yeung

Institute of Textiles and Clothing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

This paper investigates the process of human psychological perceptions of clothing- related sensations and comfort to develop an intellectual understanding of and method ology for predicting clothing comfort performance from fabric physical properties. Var ious hybrid models are developed using different modeling techniques by studying human sensory perception and judgement processes. By combining the strengths of statistics (data reduction and information summation), a neural network (self-learning ability), and fuzzy logic (fuzzy reasoning ability), hybrid models are developed to simulate different stages of the perception process. Results show that the TS-TS-NN-FL model has the highest ability to predict overall comfort performance from fabric physical properties. To summarize, the three key elements in predicting psychological perceptions of clothing comfort from fabric physical properties are data reduction and summation, self-learning, and fuzzy reasoning. This paper shows that the model that integrates these three elements can generate the best predictions compared with other hybrid models.

Textile Research Journal, Vol. 74, No. 1, 13-19 (2004)
DOI: 10.1177/004051750407400103
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