Pattern recognition systems implemented using deep neural networks achieve better results than linear models. However, their drawback is the black box property. This property means that one with no experience utilising nonlinear systems may need help understanding the outcome of the decision. Such a solution is unacceptable to the user responsible for the final decision. He must not only believe in the decision but also understand it. Therefore, recognisers must have an architecture that allows interpreters to interpret the findings. The idea of post-hoc explainable classifiers is to design an interpretable classifier parallel to the black box classifier, giving the same decisions as the black box classifier. This paper shows that the explainable classifier completes matching classification decisions with the black box classifier on the MNIST and FashionMNIST databases if Zadeh`s fuzzy logic function forms the classifier and DeconvNet importance gives the truth values. Since the other tested significance measures achieved lower performance than DeconvNet, it is the optimal transformation of the feature values to their truth values as inputs to the fuzzy logic function for the databases and recogniser architecture used.

深度神经网络实现的模式识别系统比线性模型取得更好的结果，然而它们的缺点是黑盒特性。该论文展示了可解释分类器与黑盒分类器在MNIST和FashionMNIST数据库上完成匹配分类决策，如果使用Zadeh的模糊逻辑函数作为分类器并且DeconvNet重要性给予真值作为输入，这使得解释分类器成为数据库和识别器架构的最佳特征值转换方式。

模糊逻辑函数作为非线性分类器的事后解释器