用蜜蜂算法优化神经模糊系统，仅凭4个关键参数精准预测PLA分子量。
Interpretable machine-learning for predicting molecular weight of PLA based on artificial bee colony optimization algorithm and adaptive neurofuzzy inference system
- 结合蜂群算法与模糊推理系统，自动筛选近红外光谱中关键特征。
- 仅用4个参数即实现282 Da的预测误差，精度显著提升。
- 适合材料研发与工业过程监控，可快速判断PLA质量。
本文将人工蜂群（ABC）算法与两种监督学习方法——人工神经网络（ANN）和自适应神经模糊推理系统（ANFIS）——结合，用于从近红外（NIR）光谱中进行特征选择，以预测医用聚乳酸（PLA）的分子量。在PLA挤出加工过程中，同步采集了在线NIR光谱、加工参数及设备设置数据。基于包含63组观测值和512个输入特征的数据集，需借助有效的机器学习工具进行数据解读与特征筛选，以提升预测精度。首先，将ABC优化算法与ANN/ANFIS结合，用于预测PLA分子量；其目标函数为最小化实验值与预测值之间的均方根误差（RMSE），同时最小化输入特征数量。结果表明，采用ABC-ANFIS方法可达到最低的RMSE为282 Da，且识别出四个关键参数：NIR波数6158 cm⁻¹、6310 cm⁻¹、6349 cm⁻¹以及熔融温度。研究证实，利用ABC算法与ANFIS结合，能有效选出最小特征集，在加工过程中实现高精度的PLA分子量预测。
原文摘要 · Abstract (English)
This article discusses the integration of the Artificial Bee Colony (ABC) algorithm with two supervised learning methods, namely Artificial Neural Networks (ANNs) and Adaptive Network-based Fuzzy Inference System (ANFIS), for feature selection from Near-Infrared (NIR) spectra for predicting the molecular weight of medical-grade Polylactic Acid (PLA). During extrusion processing of PLA, in-line NIR spectra were captured along with extrusion process and machine setting data. With a dataset comprising 63 observations and 512 input features, appropriate machine learning tools are essential for interpreting data and selecting features to improve prediction accuracy. Initially, the ABC optimization algorithm is coupled with ANN/ANFIS to forecast PLA molecular weight. The objective functions of the ABC algorithm are to minimize the root mean square error (RMSE) between experimental and predicted PLA molecular weights while also minimizing the number of input features. Results indicate that employing ABC-ANFIS yields the lowest RMSE of 282 Da and identifies four significant parameters (NIR wavenumbers 6158 cm-1, 6310 cm-1, 6349 cm-1, and melt temperature) for prediction. These findings demonstrate the effectiveness of using the ABC algorithm with ANFIS for selecting a minimal set of features to predict PLA molecular weight with high accuracy during processing
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