Neural network is a powerful learning paradigm for data feature learning in
the era of big data. However, most neural network models are deterministic
models that ignore the uncertainty of data. Fuzzy neural networks are proposed
to address this problem. FDNN is a hierarchical deep neural network that
derives information from both fuzzy and neural representations, the
representations are then fused to form representation to be classified. FDNN
perform well on uncertain data classification tasks. In this paper, we proposed
a novel hierarchical fused quantum fuzzy neural network (HQFNN). Different from
classical FDNN, HQFNN uses quantum neural networks to learn fuzzy membership
functions in fuzzy neural network. We conducted simulated experiment on two
types of datasets (Dirty-MNIST and 15-Scene), the results show that the
proposed model can outperform several existing methods. In addition, we
demonstrate the robustness of the proposed quantum circuit.

该论文介绍了一种新颖的分层融合量子模糊神经网络（HQFNN），与经典的 FDNN 不同，HQFNN 使用量子神经网络来学习模糊神经网络中的模糊隶属函数，模拟实验证明该模型在不确定数据分类任务上表现出色，并且还展示了所提出的量子电路的鲁棒性。

一种用于图像分类的层次融合的量子模糊神经网络

A Hierarchical Fused Quantum Fuzzy Neural Network for Image  Classification

A new concept of a multi-valued associative memory is introduced,
generalizing a similar one in fuzzy neural networks. We expand the results on
fuzzy associative memory with thresholds, to the case of a multi-valued one: we
introduce the novel concept of such a network without numbers, investigate its
properties, and give a learning algorithm in the multi-valued case. We
discovered conditions under which it is possible to store given pairs of
network variable patterns in such a multi-valued associative memory. In the
multi-valued neural network, all variables are not numbers, but elements or
subsets of a lattice, i.e., they are all only partially-ordered. Lattice
operations are used to build the network output by inputs. In this paper, the
lattice is assumed to be Brouwer and determines the implication used, together
with other lattice operations, to determine the neural network output. We gave
the example of the network use to classify aircraft/spacecraft trajectories.

介绍了一种新的多值关联记忆的概念，利用模糊神经网络作为广义基础。在多值关联记忆的情况下，基于阈值的模糊关联记忆的结果得到了扩展。探讨了这样一种在没有数字的网络中的性质，并在多值情况下给出了一种学习算法。通过格运算来建立输入的网络输出，并且我们给出了使用网络分类飞机 / 航天器轨迹的示例。