This paper proposes the definition of the architecture of an evolving fuzzy neural network based on self-organizing direction aware data partitioning through stochastic processes based on the dataset used in the model. The choice between four different types of logical neurons in the second layer and the six different types of neuron activation function that compose the artificial neural network of aggregation are defined in a procedure of generating random pairs of combinations between the two factors. The combination that obtains the maximization of training accuracy is chosen to compose the network and perform the composition of the model structure, whose components can be transferred to readable IF-THEN rules for interpretability purposes. Furthermore, the model is able to adapt its parameters and evolve its structure autonomously with new data samples through self-organized direction-aware data partitioning (SODA). In this context, we also propose a technique to measure the degree of changes of neurons (rules), which could be used for structural active learning purposes (e.g, to request user feedback in the case of significant changes). To compare the proposed approach, binary pattern classification tests were performed, and the results were compared with other models of fuzzy neural networks and neural networks, obtaining satisfactory results with the stochastic definition of elements that compose their architecture comparing the final accuracy of the model when classifying real datasets. The proposed model obtained the best result in 3 of the four synthetic bases evaluated, in addition to the best accuracy results in the classification of patterns in five of the nine evaluated real datasets. It highlights the accuracy in problems in patients who underwent breast cancer surgery (72.44%), diabetes evaluation (67.87%), Australian (72.27%) and German (80.51%) credit ratings evaluation, and finally in the classification of radar signals in the ionosphere (90.46%).

It is also noteworthy to obtain fuzzy rules in evolution extracted from a real problem of the identification of respiratory diseases through the collection of saliva with 76.67% of accuracy. The results presented by the model were superior to traditional artificial intelligence models, while it was possible to extract knowledge in form of interpretable rules and to realize how these changed over time.

本文基于模型中使用的数据集，通过随机过程提出了基于自组织方向感知数据分区的演化模糊神经网络架构的定义。在第二层中四种不同类型的逻辑神经元之间的选择和组成人工聚合神经网络的六种不同类型的神经元激活函数之间的选择是在两个因素之间生成随机组合对的过程中定义的。选择获得最大训练精度的组合来组成网络并执行模型结构的组合，其组件可以转换为可读的 IF-THEN 规则以用于可解释性目的。此外，该模型能够通过自组织方向感知数据分区 (SODA) 调整其参数并根据新数据样本自主演化其结构。在这种情况下，我们还提出了一种测量神经元（规则）变化程度的技术，该技术可用于结构性主动学习目的（例如，在发生重大变化时请求用户反馈）。为了比较所提出的方法，进行了二进制模式分类测试，并将结果与​​模糊神经网络和神经网络的其他模型进行了比较，通过对构成其架构的元素的随机定义比较模型的最终精度，获得了满意的结果对真实数据集进行分类。所提出的模型在评估的四种合成碱基中的 3 种中获得了最佳结果，除了在九个评估的真实数据集中的五个中的模式分类中的最佳准确性之外。它突出了在接受乳腺癌手术（72.44%）、糖尿病评估（67.87%）、澳大利亚（72.27%）和德国（80.51%）信用评级评估中的问题的准确性，最后在雷达信号的分类中电离层 (90.46%)。

同样值得注意的是，通过唾液采集，从一个呼吸系统疾病识别的实际问题中提取出进化中的模糊规则，准确率达到76.67%。该模型呈现的结果优于传统的人工智能模型，同时可以以可解释规则的形式提取知识，并了解这些规则是如何随时间变化的。