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
fDate :
27 Jun-2 Jul 1994
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;
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
DOI :
10.1109/ICNN.1994.374605