College of Food Science and Technology, Henan Agricultural University, Zhengzhou, Henan 450002, China.
Key Laboratory of Staple Grain Processing, Ministry of Agriculture and Rural Affairs, Zhengzhou, Henan 450002, China.
Biomed Res Int. 2021 Sep 24;2021:9202127. doi: 10.1155/2021/9202127. eCollection 2021.
This study is aimed at modeling biodigestion systems as a function of the most influencing parameters to generate two robust algorithms on the basis of the machine learning algorithms, including adaptive network-based fuzzy inference system (ANFIS) and least square support vector machine (LSSVM). The models are assessed utilizing multiple statistical analyses for the actual values and model outcomes. Results from the suggested models indicate their great capability of predicting biogas production from vegetable food, fruits, and wastes for a variety of ranges of input parameters. The values that are calculated for the mean relative error (MRE %) and mean squared error (MSE) were 29.318 and 0.0039 for ANFIS, and 2.951 and 0.0001 for LSSVM which shows that the latter model has a better ability to predict the target data. Finally, in order to have additional certainty, two analyses of outlier identification and sensitivity were performed on the input parameter data that proved the proposed model in this paper has higher reliability in assessing output values compared with the previous model.
本研究旨在将生物消化系统建模为影响因素的函数，以基于机器学习算法生成两个强大的算法，包括自适应网络模糊推理系统（ANFIS）和最小二乘支持向量机（LSSVM）。利用多种统计分析方法对实际值和模型结果进行了模型评估。所提出模型的结果表明，它们能够很好地预测各种输入参数范围内的蔬菜食品、水果和废物的沼气产量。对于平均相对误差（MRE％）和均方误差（MSE），ANFIS 的计算值为 29.318 和 0.0039，LSSVM 的计算值为 2.951 和 0.0001，这表明后者模型具有更好的预测目标数据的能力。最后，为了增加额外的确定性，对输入参数数据进行了异常值识别和敏感性分析，这证明了本文提出的模型在评估输出值方面比以前的模型具有更高的可靠性。
