In this paper, the prediction of compressive cement strength using the fuzzy linear regression (FLR) and adaptive neuro-fuzzy inference system (ANFIS) methods was studied. Specifically, an accurate prediction method is needed as the modeling of cement strength is a difficult task, which is based on its composite nature. However, many approaches are widely implemented in strength-predicting problems, such as the artificial neural network (ANN), Mamdani fuzzy rules in MATLAB, FLR and ANFIS models. Applying these methods and comparing the results with the corresponding observed ones, we concluded that the ANFIS method successfully decreased the level of uncertainty in predicting cement strength, as the average percentage error level was extremely low. Although the FLR method had the highest average percentage error level compared with the other methods, it provides a standard equation to estimate the output values by using symmetric triangular fuzzy numbers and determines the most important factor in increasing compressive strength, in contrast to ANFIS and ANN, which are black box models, and to the fuzzy method, which uses rules without providing the specific way by which the results come out. Thus, ANFIS and FLR are appropriate methods for dealing with engineering mathematical models by using fuzzy logic.

中文翻译：

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使用模糊线性回归和 ANFIS 方法预测水泥的抗压强度

在本文中，研究了使用模糊线性回归（FLR）和自适应神经模糊推理系统（ANFIS）方法预测水泥抗压强度。具体而言，需要一种准确的预测方法，因为水泥强度的建模是一项艰巨的任务，这是基于其复合性质的。然而，许多方法在强度预测问题中得到了广泛应用，例如人工神经网络 (ANN)、MATLAB 中的 Mamdani 模糊规则、FLR 和 ANFIS 模型。应用这些方法并将结果与​​相应的观察结果进行比较，我们得出结论，ANFIS 方法成功地降低了预测水泥强度的不确定性水平，因为平均百分比误差水平极低。尽管与其他方法相比，FLR 方法的平均百分比误差水平最高，但与 ANFIS 和 ANN 相比，它通过使用对称三角模糊数提供了一个标准方程来估计输出值，并确定了提高抗压强度的最重要因素，这是黑盒模型，以及模糊方法，它使用规则而不提供结果的具体方式。因此，ANFIS 和 FLR 是使用模糊逻辑处理工程数学模型的合适方法。它使用规则而不提供结果的具体方式。因此，ANFIS 和 FLR 是使用模糊逻辑处理工程数学模型的合适方法。它使用规则而不提供结果的具体方式。因此，ANFIS 和 FLR 是使用模糊逻辑处理工程数学模型的合适方法。