棉纺织技术2024，Vol.52Issue(4)：16-22,7.
基于粒子群优化支持向量机的纱线质量预测
Yarn quality prediction based on support vector machine optimized by particle swarm optimization
摘要
Abstract
In light of the insufficient prediction accuracy in yarn quality during complex spinning processes and the demand for large datasets,this study presents a small sample yarn quality prediction method by optimizing Support Vector Machines(SVR)using Particle Swarm Optimization(PSO).Firstly,the original dataset samples are preprocessed using grey relational analysis,and they are ranked based on their degrees of correlation.Combining this with prior knowledge,the primary cotton fiber features are selected.Secondly,for the small sample prediction problem,SVR prediction models with different kernel functions,including linear,polynomial,Gaussian,and adaptive bandwidth RBF kernels,are established.Finally,PSO algorithm is employed to identify the hyperparameters of the SVR models,including the regularization coefficient and bandwidth adjustment parameter.Additionally,a linearly decreasing inertia weight strategy is designed to enhance the overall optimization capability of the PSO algorithm.The simulation results show that the PSO-optimized Gauss kernel SVR model has a good prediction effect on different yarn quality indexes,and its average relative error is less than 2%.It is concluded that the PSO-optimized Gauss kernel SVR model has a low prediction error for yarn quality index and a good adaptability.关键词
支持向量机/粒子群优化/灰色关联/纱线质量预测/核函数Key words
support vector machine/particle swarm optimization/grey correlation/yarn quality prediction/kernel function分类
轻工业引用本文复制引用
章军辉,陈明亮,郭晓满,付宗杰,王静贤..基于粒子群优化支持向量机的纱线质量预测[J].棉纺织技术,2024,52(4):16-22,7.基金项目
江苏省博士后科研资助计划(2020Z411) （2020Z411）
