基于PSO优化LS-SVM的木材含水率软测量建模

木材含水率是木材干燥过程中重要的技术指标。针对木材干燥过程具有强耦合、大滞后、非线性的特点以及木材含水率检测存在的问题，提出一种软测量方法。利用最小二乘支持向量机(LS-SVM)对非线性系统时间序列数据进行学习，建立被控对象的软测量模型，同时通过粒子群优化(PSO)算法对LS-SVM的惩罚因子和核函数参数进行滚动优化，提高软测量模型的预测精度。将木材干燥窑内的温度、湿度以及木材含水率作为样本数据，通过PSO优化的LS-SVM方法建立木材含水率的软测量模型，进而利用该模型实现对目标检测点木材含水率的软测量。仿真结果表明，PSO-LSSVM软测量模型预测精度高，泛化能力强，满足木材干燥控制系统的实际测量要求。

Soft sensor model for wood moisture content based on LS-SVM optimized by PSO

Wood moisture content is an important technical specification in the wood drying process. Considering the strong coupling, large lag non-linear features of the wood drying process and the problem of low precision of wood moisture content detection, we proposed a soft sensor method using least squares support vector machines (LS-SVM) to learn time series data of a non-linear system, and built a soft sensor model of the controlled object. We also used the particle swarm optimization (PSO) algorithm in the moving horizon optimization of the penalty factor and the kernel function parameter of LS-SVM to improve the prediction precision of the soft sensor model. Taking the inner temperature and humidity of a wood drying kiln as the sample data, the wood moisture content at a specific point can be detected with the model based on LS-SVM optimized by PSO, which is denoted by PSO-LSSVM. The simulation reveals that the PSO-LSSVM has a high prediction precision and strong generalization ability, and can fulfill the actual measurement demand of a wood drying control system.