基于PSO-LSSVM的水分仪称重传感器非线性补偿研究

Study on weighing sensor's nonlinear compensation of the moisture instrument based on PSO-LSSVM

水分仪称重传感器的输出与待烘干物重量存在一定的非线性关系,且烘干失重法对传感器工作环境温度影响也会造成称重传感器输出的非线性变化。在分析传统应片式传感器非线性输出的产生机理基础上,提出一种基于变异粒子群-最小二乘支持向量机(PSO-LSSVM)的新型称重传感器非线性补偿方案:首先采用噪声协方差可变的卡尔曼滤波算法对称重传感器输出数据进行滤波,减小噪声的影响;再将滤波后的数值与环境温度值作为回归参量,建立基于LSSVM算法的水分仪称重模型;利用变异PSO算法对模型进行参数寻优。实验表明,在220 g/0.001 g水分仪中采用本方法对传感器输出进行非线性补偿后,称量精度明显优于国家检定规程标准;此外,该方法满足在小训练样本条件下,具有很好的泛化性。

There is a nonlinear relationship between the output of the moisture instrument weighing sensor and the weight of drying objects,and the working environment temperature change will also make sensors＇ nonlinear output for the loss on drying method. After analyzing the nonlinear output mechanism of the strain gauge sensor,the paper presents a new weighing sensor＇s nonlinear compensation method based on least squares support vector machine（ LSSVM） and mutated modified particle swarm optimization（ PSO）. Firstly,this method uses noise covariance variable kalman filter algorithm to filter data to reduce the noise influence. Then,establishes a regression model based on LSSVM is established for filtered data and working temperature. Finally,a mutated PSO algorithm is applied to optimize the model parameters. The experimental results show that the accuracy of the 220 g/0. 001 g moisture instrument with the proposed method is much higher than the national verification regulation standard. Moreover,the model has excellent generalization ability with small samples.