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。