Title :
Neurocomputations in relational systems
Author_Institution :
Dept. of Electr. & Comput. Eng., Manitoba Univ., Winnipeg, Man., Canada
fDate :
3/1/1991 12:00:00 AM
Abstract :
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
Keywords :
fuzzy set theory; learning systems; neural nets; complexity; composition operators; learning procedures; neural networks; neurocomputations; optimized performance index; relational systems; Biological neural networks; Classification tree analysis; Computer networks; Error analysis; Humans; Machine intelligence; Man machine systems; Pattern recognition; Performance evaluation; Regression tree analysis;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
