Support Vector Machine with Real Code Genetic Algorithm for Yarn Quality Prediction
Support Vector Machine with Real Code Genetic Algorithm for Yarn Quality Prediction
复制标题
支持向量机与实码遗传算法的纱线质量预测
DOI：
10.1166/asl.2013.4933
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发表时间：
2013-08
期刊：
影响因子：
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通讯作者：
Bei-zhi Li
中科院分区：
文献类型：
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作者：
Zhi- Jun Lv;Qian Xiang;Bei-zhi Li
纱线质量预测在现代纺织生产管理中占有重要地位。由于纱线质量指标序列的非线性和非平稳性，常用的回归分析和人工神经网络等方法的精度受到了限制。针对成纱质量预测问题，提出了一种基于支持向量回归(SVR)的预测模型。用实数编码遗传算法进行模型选择，相当于在超参数空间中搜索，以研究合适的参数C和σ。通过与神经网络模型的比较，估计了RGA-支持向量机模型的预测能力。实验结果表明，在小数据集和实际生产中，RGA-支持向量机模型能够保持预测精度的稳定性，更适合于噪声和动态纺丝过程。
Yarn quality prediction plays an important role in modern textile production management. Due to the nonlinearity and non-stationarity of yarn quality indicator series, the accuracy of the commonly used conventional methods, including regression analyses and artificial neural networks (ANN), has been limited. A prediction model based on support vector regression (SVR) is proposed in this paper to solve the yarn quality prediction problem. Model selection which amounts to search in hyper-parameter space is performed for study of suitable parameters, C and σ, with real code Genetic Algorithms (RGA). The predictive powers of the RGA-SVM models are estimated by comparison with ANN models. The experimental results indicate that in the small data sets and real-life production, the RGA-SVM models are capable of remaining the stability of predictive accuracy, and more suitable for noisy and dynamic spinning process.