Abstract In this paper, three different data-driven algorithms were employed including two nonlinear models (Artificial neural network (ANN) and Adaptive neuro-fuzzy inference system (ANFIS)) and a classical linear model (Multilinear regression analysis (MLR)) for the simulation of response surface for methyclothiazide (M) and amiloride (A) considered as (K’or k) modeling in HPCL using pH and composition of mobile phase (methanol) as the corresponding input variables. The experimental and simulated results were evaluated based on five different performance efficiency criteria namely; determination coefficient (R2), root mean square error (RMSE), correlation coefficient (R), mean square error (MSE) and mean absolute percentage error (MAPE). The obtained results demonstrated the promising ability of ANN and ANFIS over MLR models with average R-values of 0.95 in both training and testing phases. The results also indicated that, with regard to the percentage error, ANN and ANFIS models outperformed the MLR model and increased the accuracy up to 6% and 8%, respectively for K’ ​ (M) simulation, while for K’ (A), ANFIS increased the accuracy up to 5% and 4% for MLR and ANN, respectively. The overall results proved the reliability of artificial intelligence models (ANN and ANFIS) for the simulation of response surface optimization method.

中文翻译：

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使用人工智能模型在 HPLC 优化方法开发中模拟响应面：一种数据驱动的方法

摘要 本文采用了三种不同的数据驱动算法，包括两种非线性模型（人工神经网络 (ANN) 和自适应神经模糊推理系统 (ANFIS)）和经典线性模型（多元线性回归分析 (MLR)）用于使用 pH 值和流动相（甲醇）的组成作为相应的输入变量，模拟在 HPCL 中作为（K' 或 k）建模的甲噻嗪 (M) 和阿米洛利 (A) 的响应面。实验和模拟结果基于五个不同的性能效率标准进行评估，即：决定系数 (R2)、均方根误差 (RMSE)、相关系数 (R)、均方误差 (MSE) 和平均绝对百分比误差 (MAPE)。获得的结果证明了 ANN 和 ANFIS 优于 MLR 模型的能力，在训练和测试阶段的平均 R 值为 0.95。结果还表明，在百分比误差方面，ANN 和 ANFIS 模型优于 MLR 模型，对于 K' (M) 模拟，准确度分别提高了 6% 和 8%，而对于 K' (A) ，ANFIS 分别将 MLR 和 ANN 的准确度提高了 5% 和 4%。总体结果证明了人工智能模型（ANN和ANFIS）用于响应面优化方法模拟的可靠性。而对于 K' (A)，ANFIS 将 MLR 和 ANN 的准确度分别提高了 5% 和 4%。总体结果证明了人工智能模型（ANN和ANFIS）用于响应面优化方法模拟的可靠性。而对于 K' (A)，ANFIS 将 MLR 和 ANN 的准确度分别提高了 5% 和 4%。总体结果证明了人工智能模型（ANN和ANFIS）用于响应面优化方法模拟的可靠性。