Neural networks are able to learn more patterns with the incremental learning than with the correlative learning. The incremental learning is a method to compose an associative memory using a chaotic neural network. In the former work, it was found that the capacity of the network increases along with its size, with some threshold value and that it decreases over that size. The threshold value and the capacity varied by the learning parameter. In this paper, the capacity of the networks was investigated by changing the learning parameter. Through the computer simulations, it turned out that the capacity increases in proportion to the network size. Then, the error correction capability is estimated with learned patterns changing to the maximum capacity.
['Toshinori Deguchi', 'Keisuke Matsuno', 'Toshiki Kimura', 'Naohiro Ishii']
Error Correction Capability in Chaotic Neural Networks