Abstract
A hierarchical network of neurons with local receptive fields is constructed by using a mixture of clustering and supervised learning strategies. The network's performance is enhanced by built-in importance sampling and by its multiscale organization in both time and phase space domains. The method is tested on predicting chaotic time-series and on balancing a physical pendulum.
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