The Neocognitron, inspired by the mammalian visual system, is a complex neural network with numerous parameters and weights which should be trained in order to utilise it for pattern recognition. However, it is not easy to optimise these parameters and weights by gradient decent algorithms. In this paper, we present a staged training approach using evolutionary algorithms. The experiments demonstrate that evolutionary algorithms can successfully train the Neocognitron to perform image recognition on real world problems.
|Title of host publication||In: Procs of the 1999 Congress on Evolutionary Computation (CEC'99) Vol. 3|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Publication status||Published - 1999|