With the improvement of people's living standards,people have higher requirements for the comfort of household apparel.Breathability is one of the key factors affecting the comfort of household apparel and is the most concerned by household apparel consumers.At present,research on the comfort of household apparel is still in a blank period both domestically and internationally.There is a lack of research on the breathability of various household apparel fabrics with different fabric compositions and textures,and there is relatively little research on predicting the comfort of household apparel.Based on this,this article selects 58 common household apparel fabrics with different fabric compositions and textures on the market,and constructs a genetic algorithm improved BP neural network model to predict the breathability performance of household apparel.Firstly,to study the relationship between various influencing factors and air permeability of household apparel fabrics,the grey relational analysis(GRA)method was used to analyze the degree of influence of each influencing factor on the air permeability of household apparel fabrics.The factors with higher correlation were selected as input parameters for the model in this study,namely density,yarn diameter,thickness,and weight.Secondly,due to the shortcomings of BP neural network,such as proneness to local minima,slow learning rate,and long training time,this study used genetic algorithm(GA)to optimize the structural parameters of BP neural network,and constructed a genetic algorithm optimized BP(GRA-GA-BP)neural network prediction model based on grey correlation analysis.Genetic algorithm can optimize the structural parameters of the model,find the best parameter combination,and solve complex and high-dimensional problems,without being affected by local optimal solutions.58 household apparel fabrics with different fabric compositions and textures were selected,of which 42 were model training samples and 16 were test samples to validate the established model.The parameters of each factor,including fabric density,yarn diameter,thickness,weight,and air permeability,were tested as input parameters for the GRA-GA-BP neural network.The results show that the measured and predicted values of air permeability had a small error,with a relative error of between 0.80％and 28.53％,and an average relative error of 8.39％;a comparison chart between the measured and predicted values of air permeability was drawn,and it was found that the two curves are basically consistent,indicating high prediction accuracy of the model.Finally,OriginPro software was used to analyze the correlation between the measured and predicted values of air permeability,and the goodness of fit R2 was 0.976,very close to 1,indicating that the model's prediction effect is good.The prediction model has a small prediction error,good prediction effect,high prediction accuracy,good fitting effect between the measured and predicted values of air permeability,and a strong correlation between the measured and predicted values.This article enriches the research on predicting the comfort of household apparel.The model can accurately predict the breathability of household apparel fabrics to a certain extent,saving manpower and costs required for experiments.It has important reference significance for household apparel designers to design based on household apparel comfort performance.At the same time,it provides a reference route for predicting the comfort of household apparel.Researchers can start from the perspective of household apparel comfort,combine subjective and objective experiments,and construct corresponding household apparel comfort evaluation and prediction models.
fabrichousehold apparelgrey correlation analysisimproved BP neural networkbreathability prediction
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