Abstract
In this article, thermal resistance test and water vapor resistance test were experimented to obtain data of heat and humidity performance. Canonical correlation analysis was used on determining influence of basic fabric parameters on heat and humidity performance. Thermal resistance model and water vapor resistance model were established with a three-layered feedforward-type neural network. For the generalization of the network and the difficulty of determining the optimal network structure, trainbr was chosen as training algorithm to find the relationship between input factors and output data. After training and verification, the number of hidden layer neurons in the thermal resistance model was 12, and the error reached 10−3. In the water vapor resistance model, the number of hidden layer neurons was 10, and the error reached 10−3.
Highlights
Knitted fabrics were used in clothing fields such as underclothing, sports-clothing early
With knitting technology developed, knitting fabrics were gradually used in coat
To keep the style of the garment, knitted double jersey was widely used in clothing
Summary
Knitted fabrics were used in clothing fields such as underclothing, sports-clothing early. A mathematical relationship between the basic parameters of knitted double jersey and the thermal and wet comfort performance is analyzed. With the help of mathematical methods, a predictive model of thermal and wet comfort performance can be established by using the basic parameters of the fabrics.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have