DocumentCode
3247661
Title
Properties of pair associator networks
Author
Campbell, Chris
Author_Institution
Dept. of Appl. Phys., Kingston Polytech., UK
fYear
1989
fDate
0-0 1989
Abstract
Summary form only given, as follows. The properties of perceptron-like pair associators are described for the ideal case of N to infinity and independent, randomly constructed patterns. For fully and partially connected perceptron networks with Hebbian-learning rules, relations between input and output overlaps are stated in addition to conditions for a perfect error-free retrieval of target patterns. The effects of feedback are also discussed in the context of these models.<>
Keywords
learning systems; neural nets; Hebbian-learning rules; feedback; fully connected perceptron networks; learning systems; neural nets; pair associator networks; partially connected perceptron networks; perceptron-like pair associators; perfect error-free retrieval; randomly constructed patterns; Learning systems; Neural networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1989. IJCNN., International Joint Conference on
Conference_Location
Washington, DC, USA
Type
conf
DOI
10.1109/IJCNN.1989.118380
Filename
118380
Link To Document