基于正则化TSVR的甘蔗收割机切割器入土切割负载压力预测研究

Prediction of load pressure of cutter of sugarcane harvester based on regularized TSVR

为更准确、快速地对甘蔗收割机切割器入土切割时负载压力的预测,以机车行进速度、土壤含水率、土壤密度、刀盘入土深度以及甘蔗密度为模型输入,基于正则化孪生支持向量回归机(ITSVR)模型,结合基于遗传算法的粒子群优化算法对切割器负载压力进行仿真模拟预测,并将仿真结果与BP神经网络、支持向量回归(TSVR)、极限学习机(ELM)以及孪生支持向量回归(TSVR)模型进行比较分析,最后建立预测系统验证ITSVR的可行性。结果表明:测试样本中正则化孪生支持向量回归机(ITSVR)模型的RMSE、MAE、R^(2)分别为0.0154、0.0108、0.9558,模型的预测时间为5.9 ms,该模型能较准确且较快地对负载压力与影响参数的非线性曲线进行拟合;ITSVR模型较其他模型具有良好的拟合能力以及较快的预测速度;在以5%和10%相对误差作为合格指标模型评价时,ITSVR模型预测的正确率分别为66.7%和100%,有力证明ITSVR模型的可行性。研究结果可为后续的切割自动控制系统设计提供参考依据。

In order to predict the load pressure of the sugarcane harvester cutter in the soil cutting process more accurately and quickly,the locomotive speed,soil moisture content,soil density,cutter depth and sugarcane density are used as model inputs,based on regularized twin support.The vector regression machine(ITSVR)model combines the particle swarm optimization algorithm based on genetic algorithm to simulate and predict the cutter load pressure,and compares the simulation results with BP neural network,support vector regression(TSVR),extreme learning machine(ELM)and The Twin Support Vector Regression(TSVR)model was compared and analyzed,and finally the feasibility of the prediction system to verify ITSVR was established.The results show that the RMSE,MAE and R^(2) of the regularized twin support vector regression machine(ITSVR)model are 0.0154,0.0108 and 0.9558,respectively,and the prediction time of the model is 5.9 ms.The model can load the load more accurately and quickly.The pressure is fitted to the nonlinear curve affecting the parameters;the ITSVR model has better fitting ability and faster prediction speed than other models;when the 5%and 10%relative error are used as the qualified index model,the ITSVR model predicts The correct rates are 66.7%and 100%,respectively,which is a strong proof of the feasibility of the ITSVR model.The research results can provide reference for the subsequent design of automatic cutting control system.