NDLI: Genetic granular cognitive fuzzy neural networks and human brains for pattern recognition

Content Provider	IEEE Xplore Digital Library
Author	Li, J. Barrett, N. Zhang, Y.-Q. Washburn, D.A.
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA, USA (Li, J.)
Abstract	Biological neural networks in the human brain can recognize different patterns with noise by the unknown biologically cognitive pattern recognition method. Since the human brain consists of biological neural networks that are the major components performing pattern recognition, it is very interesting and very important to investigate how the biological neural networks and the artificial neural networks recognize different patterns. A new genetic granular cognitive fuzzy neural network based on granular computing, soft computing and cognitive science is used in a pattern recognition problem to compare human brains with the biological neural networks. The hybrid genetic forward-wave-backward-wave learning algorithm is used to enhance learning quality. Both pattern recognition results generated by human persons and the genetic granular cognitive fuzzy neural network are analyzed in terms of computer science and cognitive science.
Sponsorship	IEEE Comput. Intelligence Soc
Starting Page	172
Ending Page	175
File Size	788294
Page Count	4
File Format	PDF
ISBN	0780390172
DOI	10.1109/GRC.2005.1547260
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Artificial neural networks neural networks fuzzy logic Pattern recognition genetic algorithms Biological neural networks psychology granular computing Biology computing Genetics Fuzzy neural networks Computer networks Cognitive science Pattern analysis cognitive science pattern recognition
Content Type	Text
Resource Type	Article

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