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Genetic Granular Cognitive Fuzzy Neural Networks and Human Brains for Comparative Cognition

In this thesis, Genetic Granular Cognitive Fuzzy Neural Networks (GGCFNN), combining genetic algorithms (GA) and granular cognitive fuzzy neural networks (GCFNN), is proposed for pattern recognition problems. According to cognitive patterns, biological neural networks in the human brain can recognize different patterns. Since GA and neural networks represent two learning methods based on biological science, it is indispensable and valuable to investigate how biological neural networks and artificial neural networks recognize different patterns. The new GGCFNN, based on granular computing, soft computing and cognitive science, is used in the pattern recognition problems. The hybrid forward-wave-backward-wave learning algorithm, as a main learning technology in GCFNN, is used to enhance learning quality. GA optimizes parameters to make GGCFNN get better learning results. Both pattern recognition results generated by human persons and those by GGCFNN are analyzed in terms of computer science and cognitive science.

Identiferoai:union.ndltd.org:GEORGIA/oai:digitalarchive.gsu.edu:cs_theses-1006
Date12 May 2005
CreatorsLi, Jun
PublisherDigital Archive @ GSU
Source SetsGeorgia State University
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceComputer Science Theses

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