基于支持向量回归的核桃在线称重系统

为了对核桃重量进行在线检测,采用加速度传感器信号对称重传感器信号动态补偿校准和支持向量回归(SRV)预测方法,设计多传感器信息融合的核桃重量在线测试系统。对400枚核桃分别在速度为0.02,0.03,0.05 m/s条件下进行数据采集,并对采集的数据进行训练和校验,分析得到最优的核桃重量预测模型为基于线性核函数的SVR模型,较佳的测试速度为0.03 m/s。利用200枚核桃样本在0.03m/s的条件下进行实验验证,结果显示系统预测的核桃重量与核桃实际重量线性拟合的r~2为0.85,平均绝对误差为1.67g,表明该系统可以较为准确地实现核桃在线称重。

Design of walnut online weighing system based on support vector regression

In order to accurately detect walnut weight online, a walnut weight online testing system based on multi-sensor information fusion was designed, which was adopted the method of dynamic compensation and calibration to process signal collected from an acceleration sensor and symmetrical weight sensors and used the algorithm of support vector regression (SRV) to predict the walnut weight. Then, 400 walnuts were used to acquire weight data at speed of 0.02, 0.03 and 0.05 m/s respectively based on this system. Meanwhile the data were trained and verified. The better prediction model of walnut weight was determined as SVR model with linear kernel function, and the best test speed was determined as 0.03 m/s. Finally, 200 walnut samples were tested online at the speed of 0.03 m/s. The results showed that r² of linear fitting between the walnut weight prediction and their actual weight was 0.85, and the average absolute error of linear fitting was 1.67 g. The results indicated that the system can accurately online test walnut weight.