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基于竹材抗菌剂常用的成分纳米银、壳聚糖、茶多酚，利用BP神经网络建立竹材抗菌剂配方与性能模型，并通过算法优化抗菌剂有效成分配比。经试验验证，BP神经网络算法计算的抗菌剂配方与化学试验法得出的配方一致，模型在训练集、验证集和测试集上的均方误差分别为0.012、0.015和0.018，决定系数分别为0.95、0.93和0.92，表明模型具有较好的拟合效果和泛化能力。同时，对比其他常用算法模型，BP神经网络算法模型的迭代次数、训练时间和误差均较小，进一步验证了其先进性。
Abstract：A model for the formulation and performance of bamboo antibacterial agents based on the common components of nano-silver, chitosan, and tea polyphenols using the BP neural network was established in this study. The ratios of the effective components of the antibacterial agents were optimized through algorithms. Experimental verification demonstrated that the formulations of the antibacterial agents calculated by the BP neural network algorithm were consistent with those obtained through chemical experiments. The mean square errors of the model on the training set, validation set, and test set were 0.012, 0.015, and 0.018, respectively, and the determination coefficients were 0.95, 0.93, and 0.92, respectively, indicating that the model possessed superior fitting effect and generalization ability. Moreover, when compared with other commonly used algorithm models, the BP neural network algorithm model had fewer iterations, shorter training time, and lower error, verifying the advancement of this algorithm model.
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基本信息:
DOI：10.19531/j.issn1001-5299.202602003
中图分类号:TS62;TP183
引用信息:
[1]朱正坤,张付花.基于BP神经网络算法的竹材抗菌剂配方优化与性能预测[J].林产工业,2026,63(02):16-22.DOI:10.19531/j.issn1001-5299.202602003.
基金信息:
江西省教育厅科学技术研究项目“基于可见光催化的木竹生态柜橱材料创制与抗菌机制”（GJJ2405505）; 新时代职业学校名师（名匠）名校长培养计划（2023—2025年）
2026-02-20
2026-02-20