基于ANFIS的稻谷深床干燥爆腰率增值预测模型的研究

为提高稻谷干燥爆腰率增值预测的精度,采用自适应神经模糊推理系统(ANFIS)建立了稻谷深床干燥爆腰率增值预测模型.经试验数据检验,爆腰率增值预测值的最大误差为14.57%,最小误差为1.68%,平均误差为5.68%,预测精度达到了94.32%.结果分析表明,该模型泛化能力强,预测精度高且可简便预测干燥参数对稻谷干燥爆腰率增值的影响,有助于准确认识爆腰率增值随干燥参数的变化规律,为干燥参数的优选和稻谷干燥品质的控制提供了依据.

Prediction of additional crack percentage for paddy rice drying in a deep fixed-bed based on ANFIS

In order to improve the prediction accuracy of additional crack percentage for paddy rice drying in a deep fixed-bed, Adaptive-Network-based Fuzzy Inference System (ANFIS) was applied to establish a prediction model for the additional crack percentage. Through verification of the prediction model, it was determined that the maximum prediction error was 14.57%, the minimum prediction error was 1.68%, the average prediction error was 5.68% and the prediction accuracy reached 94.32%. The results of analysis show that the prediction accuracy and the generalization of the prediction model are very high, and the model can predict the effects of drying parameters on additional crack percentage conveniently, which contributed to understanding of variation of additional crack percentage impacted by drying parameters accurately and provided the foundation for selecting drying parameters properly and for controlling drying quality.