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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
