DocumentCode
3252896
Title
A complex sequence recognition model
Author
Tanaka, Takehisa
Author_Institution
Center for Neural Eng., Univ. of Southern California, Los Angeles, CA, USA
Volume
4
fYear
1992
fDate
7-11 Jun 1992
Firstpage
202
Abstract
The author proposes a biologically plausible model to learn and recognize complex sequences. He uses decreasing outputs and units which order recurring symbols. Though the model does not deal with complicated details of biological neurons, it is more biologically plausible and easier to implement on real hardware than other models. It also avoids crosstalk of memorized sequences. An arbitrary number and length of sequences can be recognized. Learning of sequences is simple and it is possible to preset weights analytically. Time periods of presenting sequences and symbols do not affect recognition
Keywords
neural nets; pattern recognition; time series; complex sequence recognition model; memorized sequences; presentation time periods; recurring symbols; temporal behaviour; temporal patterns; Abstracts; Biological system modeling; Crosstalk; Delay effects; Detectors; Equations; Hardware; Mathematical model; Neurofeedback; Neurons;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location
Baltimore, MD
Print_ISBN
0-7803-0559-0
Type
conf
DOI
10.1109/IJCNN.1992.227341
Filename
227341
Link To Document