DocumentCode
2437810
Title
Fractal neural networks for short term memory
Author
Li, X. ; Wong, W.S.
Author_Institution
Dept. of Inf. Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong
Volume
4
fYear
1994
fDate
27 Jun-2 Jul 1994
Firstpage
2454
Abstract
Synaptic connection matrix encodes limited information. It is well known that neural network memory with storage prescriptions based on Hebb´s rule will collapse as more patterns are stored. By requiring that old patterns be automatically forgotten and the memory recall only the most recently ones, a new short-term neural network memory based on Y. Baram´s fractal neural network is obtained. Comparison is drawn with Morris and Wong´s method and the experimental results are shown to be rather satisfactory and encouraging
Keywords
content-addressable storage; error statistics; fractals; learning (artificial intelligence); neural nets; error probability; fractal neural networks; learning algorithm; short term memory; synaptic matrix; Application software; Associative memory; Biological system modeling; Computer network reliability; Fractals; Neural networks; Neurofeedback; Neurons; Pattern analysis; Terminology;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location
Orlando, FL
Print_ISBN
0-7803-1901-X
Type
conf
DOI
10.1109/ICNN.1994.374605
Filename
374605
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