A neural network algorithm and its prediction model towards the full color phase mixing process of colored fibers. Issue 11 (June 2023)

• Record Type: Journal Article
• Authors: Sun, Xianqiang; Xue, Yuan; Liu, Yuexing; Wang, Liqiang; Liu, Lixia
• Is Part Of: Textile research journal. Volume 93:Issue 11/12(2023)
• Page Start: 2449
• Page End: 2463
• Publication Date: 2023-06
• Subjects: Color prediction models -- multi-channel ring spinning -- neural networks -- colored fibers -- full color phase mixing model; Textile industry -- Periodicals; Textile fabrics -- Periodicals; Textile research -- Periodicals; Textiles et tissus -- Industrie et commerce -- Périodiques; Textiles et tissus -- Périodiques; Textiles et tissus -- Recherche -- Périodiques; Electronic journals; 677.005
• DOI: 10.1177/00405175221138978
• Languages: English
• ISSNs: 0040-5175

Abstract

Aiming at the demand of color matching techniques in the spinning process, a neural network prediction model is constructed in this research study, and the gridded full color phase mixing space of colored fibers is used as the sample space. Subsequently, 30 grid points are employed as training samples, while another 30 grid points are adopted as testing samples, in which the parameters of the input, hidden, and output layers are optimized. Additionally, the neural network prediction model is constructed by training samples, and validated by testing samples. Lastly, a neural network prediction model is applied to implement the prediction of color and mixing ratios for any point within the full color phase mixing model. Through the assessment of the testing samples, the predicted results for the colors of the grid point samples showed an average color difference of 1.29 (minimum was 0.22 and maximum was 2.97); the forecasts for the mixing ratios of the colored fibers were that the range of the mean absolute error for the mixing ratios of individual samples was from 0.01% to 0.18%, and the mean absolute error for the mixing ratios of all samples was 0.21%. The experimental results indicated that the proposed neural network model has a relatively advanced prediction accuracy.