DocumentCode
2692646
Title
A neural network-based associative memory for storing complex-valued patterns
Author
Chakravarthy, Srinivasa V. ; Ghosh, Joydeep
Author_Institution
Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX, USA
Volume
3
fYear
1994
fDate
2-5 Oct 1994
Firstpage
2213
Abstract
A neural network-based associative memory for storing complex patterns is proposed. Two variations of the model are proposed: 1) a discrete model, and 2) a continuous model. The latter approaches the former as a limit. A crude capacity estimate for the discrete model is made. Network weights can be calculated in one step using a complex outer-product rule or can be adjusted adaptively using a Hebbian learning rule. Possible biological significance of the complex neuron state is briefly discussed
Keywords
Hebbian learning; adaptive systems; associative processing; content-addressable storage; neural nets; Hebbian learning rule; associative memory; complex-valued patterns storage; continuous model; discrete model; network weights; neural network; Artificial neural networks; Associative memory; Biological system modeling; Biology computing; Computer networks; Hebbian theory; Neural networks; Neurons; Stability criteria;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man, and Cybernetics, 1994. Humans, Information and Technology., 1994 IEEE International Conference on
Conference_Location
San Antonio, TX
Print_ISBN
0-7803-2129-4
Type
conf
DOI
10.1109/ICSMC.1994.400193
Filename
400193
Link To Document