Issue No. 03 - March (1991 vol. 13)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.75517
<p>Strong analogies between relational structures involving some composition operators and a certain class of neural networks are described. The problem of learning the connections of the structure is addressed, and relevant learning procedures are proposed. An optimized performance index which has a strong logical flavor is proposed. Some significant implementation details are studied. Numerical examples illustrate various schemes of learning in relational structures of different levels of complexity.</p>
neurocomputations; relational systems; composition operators; neural networks; learning procedures; optimized performance index; complexity; fuzzy set theory; learning systems; neural nets
W. Pedrycz, "Neurocomputations in Relational Systems," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 13, no. , pp. 289-297, 1991.