Abstract Reliable Artificial Intelligence methods are developed here for the determination of the surface tension of alcohols in a wide range of temperatures. A total amount of 3063 data was considered for 149 alcohols. Three methods were applied: multilayer perceptron neural network, radial basis neural network and, least-squares support-vector machine (LSSVM). Three different optimization algorithms were used for the multilayer perceptron and the support-vector machine, whereas only one was needed in the case of the radial basis neural network. So, seven different Artificial Intelligence methods were considered, trained and tested. Combination of 9 different input properties, their inverses, and their natural logarithms were studied, and the final input set chosen includes 12 of them. The obtained results were widely studied by using both graphical and common statistical tools. The applicability domain was obtained and different sensitivity analyses were performed. The method giving the lowest averaged deviations is the LSSVM optimized with Cuckoo Optimization Algorithm, the Absolute Average Relative Deviation being 0.61%.

中文翻译：

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使用人工智能方法测定纯醇表面张力

摘要 这里开发了可靠的人工智能方法，用于在很宽的温度范围内测定醇的表面张力。总共考虑了 149 种醇的 3063 个数据。应用了三种方法：多层感知器神经网络、径向基神经网络和最小二乘支持向量机 (LSSVM)。多层感知器和支持向量机使用了三种不同的优化算法，而径向基神经网络只需要一种。因此，我们考虑、训练和测试了七种不同的人工智能方法。研究了 9 个不同输入属性的组合、它们的逆函数和它们的自然对数，最终选择的输入集包括其中的 12 个。使用图形和常用统计工具对所得结果进行了广泛研究。获得了适用性域并进行了不同的敏感性分析。给出最低平均偏差的方法是使用 Cuckoo 优化算法优化的 LSSVM，绝对平均相对偏差为 0.61%。