Fuzzy-set based models of neurons and knowledge-based networks

We will introduce and study different fuzzy-set oriented computational models of neurons. The generic topologies of the neurons emerging there are significantly influenced by basic logic operators(AND, OR, NOT) encountered in the theory of fuzzy sets. The logical flavor of the proposed constructs is expressed in terms of operators used in their formalization and a way of their superposition in the neurons. The two broad categories of neurons embrace basic aggregation neurons (named AND and OR neurons) and referential processing units (such as matching, dominance, inclusion neurons). The specific features of the neurons are flexibly modeled with the aid of triangular norms. The inhibitory and excitatory characteristics are captured by embodying direct and complemented (negated) input signals. We will propose various topologies of neural networks put together with the use of these neurons and demonstrate straightforward relationships coming off between the problem specificity and the resulting architecture of the network. This limpid way of mapping the domain knowledge onto the structure of the network contributes significantly toward enhancements in learning processes in the network and substantially facilitates its interpretation