Improved Chaotic Associative Memory for Successive Learning

Recently, neural networks are drawing much attention as a method to realize flexible information processing. Neural networks consider neuron groups of the brain in the creature, and imitate these neurons technologically. Neural networks have some features, especially one of the important features is that the networks can learn to acquire the ability of information processing. In the filed of neural network, many models have been proposed such as the Back Propagation algorithm (Rumelhart et al., 1986), the Self-Organizing Map (Kohonen, 1994), the Hopfield network (Hopfield, 1982) and the Bidirectional Associative Memory (Kosko, 1988). In these models, the learning process and the recall process are divided, and therefore they need all information to learn in advance. However, in the real world, it is very difficult to get all information to learn in advance. So we need the model whose learning and recall processes are not divided. As such model, Grossberg and Carpenter proposed the Adaptive Resonance Theory (ART) (Carpenter & Grossberg, 1995). However, the ART is based on the local representation, and therefore it is not robust for damage. While in the field of associative memories, some models have been proposed (Watanabe et al., 1995; Osana & Hagiwara, 1999; Kawasaki et al., 2000; Ideguchi et al., 2005). Since these models are based on the distributed representation, they have the robustness for damaged neurons. However, their storage capacity is very small because their learning processes are based on the Hebbian learning. In contrast, the Hetero Chaotic Associative Memory for Successive Learning with give up function (HCAMSL) (Arai & Osana, 2006) and the Hetero Chaotic Associative Memory for Successive Learning with Multi-Winners competition (HCAMSL-MW) (Ando et al., 2006) have been proposed in order to improve the storage capacity. In this research, we propose an Improved Chaotic Associative Memory for Successive Learning (ICAMSL). The proposed model is based on the Hetero Chaotic Associative Memory for Successive Learning with give up function (HCAMSL) (Arai & Osana, 2006) and the Hetero Chaotic Associative Memory for Successive Learning with Multi-Winners competition (HCAMSL-MW) (Ando et al., 2006). In the proposed ICAMSL, the learning process and recall process are not divided. When an unstored pattern is given to the