Chaotic associative memory for successive learning using internal patterns

The authors propose a chaotic associative memory for successive learning (CAMSL) using internal patterns. In the CAMSL, the learning process and the recall process are not divided. When an unstored pattern is given to the network, the CAMSL can learn the pattern successively. The CAMSL distinguishes an unstored pattern from the stored patterns. When a stored pattern is given, the CAMSL recalls the pattern. When an unstored pattern is given, the CAMSL changes the internal pattern for the input pattern by chaos and presents the other pattern candidates. When the CAMSL cannot recall the desired pattern, it learns the input pattern as an unstored pattern. We carried out a series of computer simulations and confirmed the effectiveness of the CAMSL