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.

翻译：本文提出了一种多值联想记忆的新概念，推广了模糊神经网络中的类似概念。我们将带阈值的模糊联想记忆结果扩展至多值情形：引入了一种无数字网络的新颖概念，研究了其性质，并给出了多值情况下的学习算法。我们发现了在这种多值联想记忆中能够存储给定网络变量模式对的条件。在多值神经网络中，所有变量并非数字，而是晶格的元素或子集，即它们仅具有部分序关系。利用晶格运算根据输入构建网络输出。本文假设该晶格为布劳威尔晶格，并确定了用于神经网络输出的蕴含运算，该运算与其他晶格运算共同作用。我们给出了该网络用于飞行器/航天器轨迹分类的实例。