Abstract
The study is aimed at the development of neural networks useful in modelling processes of fuzzy decision-making. A variety of logic-oriented neurons (both aggregative and referential processing units) makes it possible to directly treat the efficacies of the decision problem at hand and handle them within the topology of the network. The learning capabilities of the decision networks are thoroughly investigated. Several aspects of these architectures including dynamical characteristics of the decision processes and representing and processing of incomplete or uncertain information are also addressed.
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