Neurocomputations in relational systems

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.<
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