GA-based supervised learning of Neocognitron

Supervised learning of Neocognitron is fulfilled by presenting training patterns, which map to specified features. However, the training patterns and many parameters are designed empirically and set manually in Fukushima´s Neocognitron. In this paper, we use genetic algorithms (GAs) to tune the parameters of Neocognitron and search its reasonable training pattern sets. First of all, the correlation amongst the training patterns is considered as a critical factor affecting Neocognitron´s performance, but it is ignored in the design of the original Neocognitron. Then, a GA-based supervised learning of the Neocognitron is proposed to tune the parameters and search training patterns. The results prove that the performance of a Neocognitron is sensitive to its training patterns, selectivity and receptive fields, and can be improved by this supervised learning on the basis of GAs and correlation analysis