非同期セルオートマトンによるリザバーコンピューティング

抄録

The asynchronously tuned cellular automaton (AT_ECA) we proposed has been shown to generate critical spatiotemporal patterns without fine-tuning of order parameters. In this study, we propose a learning system that applies the remarkable characteristics of AT_ECA to reservoir computing, which has recently been attracting attention as a learning model for time series data. Then, the learning ability of the proposed system was evaluated by the learning task called five-bit task. As a result, it became clear that the success rate of learning is relatively high even if distractor in time series data to learn is long.