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Due to the rapid technological evolution and communications accessibility, data generated from different sources of information show an exponential growth behavior. That is, volume of data samples that need to be analyzed are getting larger, so the methods for its processing have to adapt to this condition, focusing mainly on ensuring the computation is efficient, especially when the analysis tools are based on computational intelligence techniques. As we know, if you do not have a good control of the handling of the volume of the data, some techniques that are based on learning iterative processes could represent an excessive load of computation and could take a prohibitive time in trying to find a solution that could not come close to desired. There are learning methods known as full batch, online and mini-batch, and they represent a good strategy to this problem since they are oriented to the processing of data according to the size or volume of available data samples that require analysis. In this first approach, synthetic datasets with a small and medium volume were used, since the main objective is to define its implementation and in experimentation phase through regression analysis obtain information that allows us to assess the performance and behavior of different learning methods under distinct conditions. To carry out this study, a Mamdani based neuro-fuzzy system with center-of-sets defuzzification with support of multiple inputs and outputs was designed and implemented that had the flexibility to use any of the three learning methods, which were implemented within the training process. Finally, results show that the learning method with best performances was Mini-Batch when compared to full batch and online learning methods. The results obtained by mini-batch learning method are as follows; mean correlation coefficient [Formula: see text] with 0.8268 and coefficient of determination [Formula: see text] with 0.7444, and is also the method with better control of the dispersion between the results obtained from the 30 experiments executed per each dataset processed.

由于技术的快速发展和通信的普及，来自不同信息源的数据呈现出指数级增长的趋势。也就是说，需要分析的数据样本量越来越大，因此处理数据的方法必须适应这种情况，主要侧重于确保计算的效率，尤其是当分析工具基于计算智能技术时。

众所周知，如果不能很好地控制数据量的处理，一些基于学习迭代过程的技术可能会代表计算的过度负载，并且可能需要花费大量时间来寻找一个无法接近理想的解决方案。有一些被称为全批量、在线和小批量的学习方法，它们是解决这个问题的一个很好的策略，因为它们根据需要分析的可用数据样本的大小或体积来处理数据。在这种首次尝试中，使用了小批量和中批量的合成数据集，因为主要目的是定义其实现，并在实验阶段通过回归分析获得信息，这些信息使我们能够评估不同学习方法在不同条件下的性能和行为。

为了进行这项研究，设计并实现了一个基于 Mamdani 的神经模糊系统，该系统具有基于集合中心的去模糊化和对多个输入和输出的支持，具有灵活性，可以在训练过程中使用这三种学习方法中的任何一种。

最后，结果表明，与全批量和在线学习方法相比，批量学习方法的性能更好。小批量学习方法的结果如下：平均相关系数[Formula: see text]为 0.8268，决定系数[Formula: see text]为 0.7444，也是 30 个数据集处理的每个数据集执行 30 次实验中，结果之间的离散性控制最好的方法。