基于SPASSABP的小麦秸秆含水率检测模型

Prediction Model of Wheat Straw Moisture Content Based on SPASSABP

为提高基于电容法的小麦秸秆含水率检测模型的检测精度,扩大含水率检测范围,提高模型适应性,本文以小麦秸秆为研究对象,使用LCR数字电桥,测量含水率为10.43%~25.89%的秸秆在频率0.05~100 kHz、容积密度90.03~179.42 kg/m^(3)和温度25~40℃内的电容,利用连续投影法(Successive projections algorithm,SPA)和主成分分析法(Principal component analysis,PCA)对原始数据进行预处理,提取特征频率,选用反向传播神经网络(Back propagation neural network,BPNN)在全频率及2个特征频率下分别建立秸秆含水率、容积密度、温度的定量分析模型,引入麻雀搜索算法(Sparrow search algorithm,SSA)优化反向传播神经网络模型。试验结果表明,基于全频率构建的模型较基于SPA算法构建的模型预测效果略好,综合考虑模型复杂度和预测性能,本研究选用基于SPA算法结合SSA算法优化后的BP神经网络模型(SPASSABP)作为小麦秸秆含水率的检测模型,其预测集R^(2)_(P)、RMSEP和RPDP分别为0.9832、0.00550和7.715。利用该模型对13个含水率为10.62%~25.59%的秸秆样本进行预测,含水率预测结果的相对误差为-5.27%~5.52%,其中96.8%的预测误差在±5%以内。由此说明,模型具有较高的准确性和较好的鲁棒性。

In order to improve the detection accuracy of the wheat straw moisture content prediction model based on capacitance method,expand the detection range of moisture content and improve the adaptability of the model,taking wheat straw as the research object and using LCR digital bridge,the capacitance data of straw with 10.43%~25.89%moisture content were measured in the frequency range of 0.05~100 kHz,volume density range of 90.03~179.42 kg/m^(3) and temperature range of 25~40℃.The original data were preprocessed by using the successive projections algorithm(SPA)and principal component analysis(PCA)to extract characteristic frequencies,BP neural network was used to establish quantitative analysis models of straw moisture content,volume density,temperature and capacitance at full frequency and two characteristic frequencies respectively,and sparrow search algorithm(SSA)was introduced to optimize the BP neural network model.The experimental results showed that the prediction effect of the model based on full frequency was slightly better than that of the model based on SPA algorithm.Considering the model complexity and prediction performance,the BP neural network model(SPASSABP)optimized based on SPA algorithm and SSA algorithm was selected as the prediction model of wheat straw moisture content.The R^(2)_(P),RMSEP and RPDP of prediction sets were 0.9832,0.00550 and 7.715,respectively.The model was used to predict 13 straw samples with water content ranging from 10.62%to 25.59%,and the relative error of water content prediction results was within-5.27%to 5.52%,96.8%of which was within±5%.This showed that the model had high accuracy and good robustness,and the method can provide an idea and theoretical reference for other crop straw water content prediction.