This work presents a new meta-heuristic approach to select the structure of polynomial NARX models for regression and classification problems. The method takes into account the complexity of the model and the contribution of each term to build parsimonious models by proposing a new cost function formulation. The robustness of the new algorithm is tested on several simulated and experimental system with different nonlinear characteristics. The obtained results show that the proposed algorithm is capable of identifying the correct model, for cases where the proper model structure is known, and determine parsimonious models for experimental data even for those systems for which traditional and contemporary methods habitually fails. The new algorithm is validated over classical methods such as the FROLS and recent randomized approaches.

中文翻译：

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元模型结构选择：为回归和分类构建多项式 NARX 模型

这项工作提出了一种新的元启发式方法来为回归和分类问题选择多项式 NARX 模型的结构。该方法通过提出新的成本函数公式来考虑模型的复杂性和每项对构建简约模型的贡献。在多个具有不同非线性特性的模拟和实验系统上测试了新算法的鲁棒性。获得的结果表明，所提出的算法能够在正确模型结构已知的情况下识别正确的模型，并为实验数据确定简约模型，即使对于那些传统和现代方法习惯性失败的系统也是如此。新算法在经典方法（如 FROLS 和最近的随机方法）上得到验证。