In this work, four prompt and robust techniques have been used to introduce new generalized models for estimation of the physical properties of pure substances, including molecular weight and acentric factor. These methods were developed based on radial basis function (RBF) neural networks, group method of data handling (GMDH), multilayer perceptron (MLP), and least square support vector machine (LSSVM) techniques. Models were introduced based on a set of experimental data including 563 pure compounds that were collected from available literature. Input parameters for estimation of molecular weight were considered as specific gravity and normal boiling point. Critical temperature, critical pressure and normal boiling point were selected as inputs for estimation of the acentric factor. Statistical and graphical error analyses normal boiling point revealed that all of the developed models are accurate. The designed RBF models give the most accurate results with an AAPRE of 5.98% and 1.92% for molecular weight and acentric factor, respectively. The developed GMDH models are in the form of simple correlations, which can be used easily in hand calculation problems without any need to computers. Comparison of the developed models with the available methods showed that all of the developed models are more accurate than the existing methods. Using the relevancy factor, the impact of each input parameter on the output results was determined. Additionally, to find out the applicability region of the developed models, and to demonstrate the reliability of the models, the Leverage method has been used. There are few data out of the applicability domain of the proposed models. All the statistical and graphical resolutions, demonstrate the reliability of the developed models in estimating the molecular weight and acentric factor.

在这项工作中，已经使用了四种快速而强大的技术来引入新的通用模型来估算纯物质的物理性质，包括分子量和无心因子。这些方法是基于径向基函数（RBF）神经网络，数据处理的分组方法（GMDH），多层感知器（MLP）和最小二乘支持向量机（LSSVM）技术开发的。基于一组实验数据引入了模型，包括从现有文献中收集的563种纯化合物。用于估计分子量的输入参数被认为是比重和正常沸点。选择临界温度，临界压力和正常沸点作为估计偏心因子的输入。统计和图形误差分析正常沸点表明，所有开发的模型都是准确的。设计的RBF模型给出最准确的结果，分子量和无心因子的AAPRE分别为5.98％和1.92％。所开发的GMDH模型具有简单的相关性形式，可以轻松地在手工计算问题中使用，而无需任何计算机。将开发的模型与可用方法进行比较表明，所有开发的模型都比现有方法更准确。使用相关因子，确定每个输入参数对输出结果的影响。此外，为了找出所开发模型的适用范围并证明模型的可靠性，已使用了杠杆方法。几乎没有数据超出所提出模型的适用范围。所有的统计和图形分辨率，都证明了开发的模型在估计分子量和无心因子方面的可靠性。