Multi-Valued Neural Networks I A Multi-Valued Associative Memory
This work addresses pattern storage and classification in neural networks for specialized domains like aerospace, but it appears incremental as it builds on existing fuzzy associative memory concepts.
The paper introduces a multi-valued associative memory that generalizes fuzzy neural networks by using lattice elements instead of numbers, and presents conditions for storing patterns and a learning algorithm. It demonstrates the network's application in classifying aircraft/spacecraft trajectories.
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.