DocumentCode
1493272
Title
Neurocomputations in relational systems
Author
Pedrycz, W.
Author_Institution
Dept. of Electr. & Comput. Eng., Manitoba Univ., Winnipeg, Man., Canada
Volume
13
Issue
3
fYear
1991
fDate
3/1/1991 12:00:00 AM
Firstpage
289
Lastpage
297
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;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/34.75517
Filename
75517
Link To Document