This paper employs dispersed nanoparticles (NPs) to build an adaptive neuro-fuzzy system (ANFIS) for predicting their thermal conductivity (TC) and viscosity according to the most important input data including concentration, size, the thickness of the interfacial layer, and intensive properties of NPs. In this regard, we gather an extensive and comprehensive data set from different sources. Here, the ANFIS model factors are optimized by using the particle swarm optimization (PSO) technique. Afterward, the obtained results are compared with previously published models which did a better job in predicting target values. In the following, to investigate the validity of our proposed model, statistical and graphical techniques are employed and it was proved that this model is efficient to evaluate the output values. Amounts of results obtained from the PSO-ANFIS model evaluation are 0.988 and 0.985 for the R2 and 0.0156 and 0.0876 for root mean squared error (RMSE) of TC ratio and viscosity ratio values, respectively, letting out a valid forecast of targets. Finally, by performing various statistical analyzes, it can be said that this model shows a high ability to predict target values and can be considered a good alternative to previous models.

中文翻译：

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提出一种基于自适应神经模糊系统的群体概念方法来预测纳米流体的物理性质

本文采用分散的纳米粒子（NPs）构建自适应神经模糊系统（ANFIS），根据最重要的输入数据（包括浓度、尺寸、界面层厚度和强度）来预测它们的热导率（TC）和粘度。 NP的属性。在这方面，我们从不同来源收集了广泛而全面的数据集。在这里，ANFIS 模型因子通过使用粒子群优化 (PSO) 技术进行优化。之后，将获得的结果与之前发布的模型进行比较，这些模型在预测目标值方面做得更好。在下文中，为了研究我们提出的模型的有效性，采用了统计和图形技术，证明了该模型对评估输出值是有效的。从 PSO-ANFIS 模型评估中获得的结果量分别为 R2 为 0.988 和 0.985，TC 比和粘度比值的均方根误差 (RMSE) 分别为 0.0156 和 0.0876，从而对目标进行了有效预测。最后，通过进行各种统计分析，可以说该模型显示出很高的目标值预测能力，可以说是之前模型的一个很好的替代品。