Title :
Properties of pair associator networks
Author_Institution :
Dept. of Appl. Phys., Kingston Polytech., UK
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;
Conference_Titel :
Neural Networks, 1989. IJCNN., International Joint Conference on
Conference_Location :
Washington, DC, USA
DOI :
10.1109/IJCNN.1989.118380
