This paper formulates a fuzzy logic neuron that uses n-uninorms to construct uni-nullneurons. A fuzzy neural network (FNN) composed of these neurons is easy to operate with nullnorms and uninorms at different times, which results in high accuracy of the model outputs and increases the flexibility in connecting the rule antecedents (enabling AND and OR connections within one rule). This, in turn, may allow experts/operators to extract more knowledge from data. The FNN uses a one-versus-rest classifier learning scheme for multi-class classification problems, where neuron activation levels construct the (indicator) regression matrix; this results in a non-linear regression by indicator, which can resolve the inherent class masking problem apparent in the linear case. We propose a specific neuron-selection strategy in the learning stage that applies Lasso to bootstrap replications in order to ensure that the rule base is as compact as possible and induced by a low number of neurons. To evaluate the new neuron acting in FNNs, we performed pattern classification, and regression tests. Compared with traditional FNN in the literature, our variant showed improved model accuracies for several high-dimensional real-world datasets in binary and multi-class classification and regression problems. Combined with the ability to generate human-readable rules, this offers the ability to generate parsimonious responses with a high degree of confidence.

本文制定了一个模糊逻辑神经元，该神经元使用 n-uninorms 来构建 uni-nullneurons。由这些神经元组成的模糊神经网络 (FNN) 易于在不同时间对零范数和不范数进行运算，从而提高模型输出的准确性并增加连接规则前提的灵活性（在一个规则内启用 AND 和 OR 连接） ）。反过来，这可以允许专家/操作员从数据中提取更多知识。FNN 对多类分类问题使用 one-versus-rest 分类器学习方案，其中神经元激活级别构建（指标）回归矩阵；这导致指标的非线性回归，这可以解决线性情况下明显的固有类屏蔽问题。我们在学习阶段提出了一种特定的神经元选择策略，将 Lasso 应用于引导复制，以确保规则库尽可能紧凑并由少量神经元诱导。为了评估在 FNN 中起作用的新神经元，我们进行了模式分类和回归测试。与文献中的传统 FNN 相比，我们的变体在二元和多类分类和回归问题中对几个高维真实世界数据集显示出改进的模型精度。结合生成人类可读规则的能力，这提供了生成具有高度置信度的简约响应的能力。我们进行了模式分类和回归测试。与文献中的传统 FNN 相比，我们的变体在二元和多类分类和回归问题中对几个高维真实世界数据集显示出改进的模型精度。结合生成人类可读规则的能力，这提供了生成具有高度置信度的简约响应的能力。我们进行了模式分类和回归测试。与文献中的传统 FNN 相比，我们的变体在二元和多类分类和回归问题中对几个高维现实世界数据集显示出改进的模型精度。结合生成人类可读规则的能力，这提供了生成具有高度置信度的简约响应的能力。