DocumentCode :
2664249
Title :
A hierarchical neural network for temporal pattern recognition
Author :
Yeap, T.H. ; Zaky, S.G. ; Tsotsos, J.K. ; Kwan, H.C.
Author_Institution :
Toronto Univ., Ont., Canada
fYear :
1990
fDate :
1-3 May 1990
Firstpage :
2967
Abstract :
A hierarchical neural network that can be trained to recognize multiple sequences of temporal events is presented. The network modules are suitable for VLSI implementation. A motion detector based on a hierarchical network was simulated and tested. The simulation results show that modules can be concatenated to recognize a long temporal sequence. Results also show that modules at higher levels in the hierarchy can be trained to recognize the temporal order of the sequences presented to modules in the lower levels. The network can be used for temporal recognition of a number of long sequences
Keywords :
VLSI; learning systems; neural nets; pattern recognition; VLSI implementation; hierarchical neural network; long temporal sequence; motion detector; multiple sequences; network modules; temporal events; temporal pattern recognition; training; Computational modeling; Computer science; Concatenated codes; Detectors; Motion detection; Neural networks; Pattern recognition; Physiology; Testing; Very large scale integration;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits and Systems, 1990., IEEE International Symposium on
Conference_Location :
New Orleans, LA
Type :
conf
DOI :
10.1109/ISCAS.1990.112633
Filename :
112633
Link To Document :
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