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

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.