Abstract
Chaotic behaviors are often shown in the biological brains. They are related strongly to the memory storage and learning in the chaotic neural networks. The incremental learning is a method to compose an associative memory using a chaotic neural network and provides larger capacity than the Hebbian rule in compensation for amount of computation. In the former works, patterns were generated randomly to have plus 1 in half of elements and minus 1 in the others. When finely-tuned parameters were used, the network learned these pattern features, well. But, this result could be taken as an over-learning. Then, we proposed pattern generating methods to avoid over-learning and tested the patterns, in which the ratio of plus 1 and minus 1 is different from 1 to 1. In this paper, our simulations investigate the capacity of the usual chaotic neural network and that of the simplified chaotic neural network with these patterns to ensure no over-learning.
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Deguchi, T., Ishii, N. (2018). On Capacity with Incremental Learning by Simplified Chaotic Neural Network. In: Fagan, D., MartÃn-Vide, C., O'Neill, M., Vega-RodrÃguez, M.A. (eds) Theory and Practice of Natural Computing. TPNC 2018. Lecture Notes in Computer Science(), vol 11324. Springer, Cham. https://doi.org/10.1007/978-3-030-04070-3_29
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