An algorithm is proposed that combines nonlinear feature generation and sparse regression to learn interpretable nonlinear models from noisy and limited data. This Algebraic Learning Via Elastic Net for Static and Dynamic Nonlinear Model Identification algorithm employs automated feature generation including families of ubiquitous chemical and biological nonlinear transformations. ALVEN balances model complexity and prediction accuracy through a two-step feature selection procedure, to produce an interpretable model useful for process applications while avoiding overfitting. The generalization to nonlinear dynamical systems, Dynamic ALVEN, is then described. The model accuracy of the algorithms is compared to well-established machine learning methods for a 3D printer and a chemical reactor.

提出了一种将非线性特征生成与稀疏回归相结合的算法，以从嘈杂和有限的数据中学习可解释的非线性模型。这种通过弹性网络进行的代数学习，用于静态和动态非线性模型识别算法，采用了自动特征生成功能，包括无处不在的化学和生物非线性变换族。ALVEN通过两步特征选择程序来平衡模型的复杂性和预测精度，以生成可解释的模型，该模型可用于过程应用，同时避免过度拟合。然后描述了对非线性动力学系统，动态ALVEN的推广。将算法的模型准确性与针对3D打印机和化学反应器的公认的机器学习方法进行比较。