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Kuremoto et al. proposed a multi-layer chaotic neural network (MCNN) combined multiple Adachi et al.'s CNNs to realize mutual auto-association of plural time series patterns. However, the MCNN was limited in a two-layer model. In this paper, we extend the MCNN to be a general form (GMCNN) with more layers and use particle swarm optimization (PSO) to improve the recollection performance of GMCNN. The recollecting characteristics by different parameter-control methods were investigated by computer simulations.

The Recollection Characteristics of Generalized MCNN Using Different Control Methods