Cotton is one of the world’s most common natural clothing materials. It is dyed mainly using the exhaustion, cold pad-batch, and pad-dry-pad-steam dyeing methods. The K/S value, an important index for measuring the depth of color, of cotton fabric dyed with reactive dyes is greatly influenced by various factors of the dyeing process. In this study, three models were developed incorporating least squares support vector machine (LSSVM) to predict the K/S values of dyed cotton fabrics, while particle swarm optimization (PSO) was applied to optimize and tune the parameters of the LSSVM model (PSO-LSSVM). Model inputs include dye concentration and process conditions, which are both easily obtainable variables. The K/S values from the PSO-LSSVM model are consistent with actual measured K/S values of dyed cotton fabrics. Moreover, a comparison among PSO-LSSVM, LSSVM and back propagation neural network results shows the superiority of the PSO-LSSVM approach. Results of this work indicate that a PSO-LSSVM model is a powerful tool for predicting the K/S value in cotton fabric dyed with reactive dye and thus a means to improve production processes and reduce costs.

中文翻译：

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基于PSO-LSSVM的棉织物K/S值预测

棉花是世界上最常见的天然服装材料之一。染色主要采用浸染、冷轧堆、轧干轧蒸等染色方法。活性染料染色的棉织物的K/S值是衡量色深的重要指标，受染色过程中的各种因素影响很大。在这项研究中，开发了三个模型，结合最小二乘支持向量机 (LSSVM) 来预测染色棉织物的 K/S 值，同时应用粒子群优化 (PSO) 来优化和调整 LSSVM 模型 (PSO) 的参数-LSSVM）。模型输入包括染料浓度和工艺条件，它们都是容易获得的变量。PSO-LSSVM 模型的 K/S 值与染色棉织物的实际测量 K/S 值一致。而且，PSO-LSSVM、LSSVM 和反向传播神经网络结果之间的比较表明 PSO-LSSVM 方法的优越性。这项工作的结果表明，PSO-LSSVM 模型是预测用活性染料染色的棉织物的 K/S 值的有力工具，因此是改进生产工艺和降低成本的一种手段。