[2] [2] FATTAHI S, RAVANDI S A H, TAHERI S M. Two-way prediction of cotton yarn properties and fiber properties using multivariate multiple regression[J]. Journal of the Textile Institute, 2011, 102(10): 849-856.

[4] [4] REYEN M E, KADOGLU H. Regressional estimation of ring cotton yarn properties from HVI fiber properties[J]. Textile Research Journal, 2006, 76(5): 360-366.

[7] [7] HASANI H, TABATABAEI S A, AMIRI G. Grey relational analysis to determine the optimum process parameters for open-end spinning yarns[J]. Journal of Engineered Fibers and Fabrics, 2012, 7(2): 155892501200700212.

[8] [8] HUSSAIN T, ARAIN F A, MALIK Z A. Use of Taguchi method and grey relational analysis to optimize multiple yarn characteristics in open-end rotor spinning[J]. Autex Research Journal, 2017, 17(1): 67-72.

[9] [9] YIN X, YU W. The virtual manufacturing model of the worsted yarn based on artificial neural networks and grey theory[J]. Applied Mathe-matics and Computation, 2007, 185(1): 322-332.

[10] [10] JUN Z. The development and application of support vector machine[J]. Journal of Physics: Conference Series, 2021, 1748(5): 052006.

[14] [14] DORAN E C, SAHIN C. The prediction of quality characteristics of cotton/elastane core yarn using artificial neural networks and support vector machines[J]. Textile Research Journal, 2020, 90(13/14): 1558-1580.

[15] [15] ABAKAR K A A, YU C. Performance of SVM based on PUK kernel in comparison to SVM based on RBF kernel in prediction of yarn tenacity[J]. Indian Journal of Fibre & Textile Research, 2014, 39(1): 55-59.

[17] [17] MWASIAGI J I, HUANG X B, WANG X H. Performance of neural network algorithms during the prediction of yarn breaking elongation[J]. Fibers and Polymers, 2008, 9: 80-86.

[18] [18] AMIN A E. A novel classification model for cotton yarn quality based on trained neural network using genetic algorithm[J]. Knowledge-Based Systems, 2013, 39: 124-132.

[19] [19] MALIK S A, FAROOQ A, GEREKE T, et al. Prediction of blended yarn evenness and tensile properties by using artificial neural network and multiple linear regression[J]. Autex Research Journal, 2016, 16(2): 43-50.

[20] [20] GHANMI H, GHITH A, BENAMEUR T. Ring spun yarn quality prediction using hybrid neural networks[J]. The Journal of the Textile Institute, 2023, 114(1): 66-74.

[21] [21] SONG J, FAN T. Yarn hairiness prediction by generalized regression neural network based on harris hawk optimization[J]. Journal of the Institution of Engineers (India): Series E, 2022, 103(2): 347-355.

[22] [22] GEIHEINI E A, ELKATEB S, ELHAMIED A M R. Yarn tensile properties modeling using artificial intelligence[J]. Alexandria Engineering Journal, 2020, 59(6): 4435-4440.

[28] [28] MAJUMDAR A. Modeling of cotton yarn hairiness using adaptive neuro-fuzzy inference system[J]. Fibre & Textile Research, 2010, 35: 121-127.

[29] [29] ADMUTHE L S, APTE S. Adaptive neuro-fuzzy inference system with subtractive clustering: a model to predict fiber and yarn relationship[J]. Textile Research Journal, 2010, 80(9): 841-846.

[33] [33] CAO W, WANG X, MING Z, et al. A review on neural networks with random weights[J]. Neurocomputing, 2018, 275: 278-287.

[34] [34] HU Z, ZHAO Q, WANG J. The prediction model of worsted yarn quality based on CNN-GRNN neural network[J]. Neural Computing and Applications, 2019, 31: 4551-4562.

[35] [35] WANG M, WANG J, GAO W, et al. One-dimensional convolutional neural network with data characterization measurement for cotton yarn quality prediction[J]. Cellulose, 2023, 30(6): 4025-4039.

[36] [36] HADAVANDI E, MOSTAFAYI S, SOLTANI P. A grey wolf optimizer-based neural network coupled with response surface method for modeling the strength of siro-spun yarn in spinning mills[J]. Applied Soft Computing, 2018, 72: 1-13.

[37] [37] JIANG H, SONG J, ZHANG B, et al. Prediction of yarn unevenness based on BMNN[J]. Journal of Engineered Fibers and Fabrics, 2021, 16: 15589250211037978.