Models based on computational intelligence have many applications in various problems. Such systems are generally set up and designed based on a collection of data and information. In some real problems, the implementation of experimental studies or doing tests are costly and time-consuming. Therefore, a model which requires fewer data than the existing soft computing methods can be useful and applicable. In this article, a network-based interactional connection system is proposed as a new supervised machine learning computational framework for problems with small data. This model, which is inspired by the connections between neurons of the brain, utilizes the series and parallel structures with interactional connections to determine the best estimation. The proposed approach uses less unknown parameters than existing models and gives a suitable response in a few steps. The learning process starts with one generation and continues until an acceptable prediction is found, and the coefficients are determined. However, it may have more generations in a sequential format for better prediction and providing more accurate answers. To evaluate the performance of the proposed computational system, three engineering problems were investigated as a numerical study. The results also compared with the predicted values of four well-known techniques.

中文翻译：

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NIC：一种创新的监督机器学习计算框架，具有基于网络的交互连接

基于计算智能的模型在各种问题中有许多应用。此类系统通常是基于数据和信息的收集来建立和设计的。在一些实际问题中，实施实验研究或进行测试是昂贵且耗时的。因此，一个比现有软计算方法需要更少数据的模型可能是有用和适用的。在本文中，提出了一种基于网络的交互连接系统，作为一种新的有监督机器学习计算框架，用于解决小数据问题。该模型受到大脑神经元之间连接的启发，利用具有交互连接的串联和并联结构来确定最佳估计。所提出的方法比现有模型使用更少的未知参数，并在几个步骤中给出合适的响应。学习过程从一代开始，一直持续到找到可接受的预测，并确定系数。但是，它可能有更多的顺序格式的生成，以便更好地预测并提供更准确的答案。为了评估所提出的计算系统的性能，研究了三个工程问题作为数值研究。结果还与四种众所周知的技术的预测值进行了比较。它可能有更多的顺序格式的生成，以便更好地预测并提供更准确的答案。为了评估所提出的计算系统的性能，研究了三个工程问题作为数值研究。结果还与四种众所周知的技术的预测值进行了比较。它可能有更多的顺序格式的生成，以便更好地预测并提供更准确的答案。为了评估所提出的计算系统的性能，研究了三个工程问题作为数值研究。结果还与四种众所周知的技术的预测值进行了比较。