Phase-Space Learning

Existing recurrent net learning algorithms are inadequate. We in(cid: 173) troduce the conceptual framework of viewing recurrent training as matching vector fields of dynamical systems in phase space. Phase(cid: 173) space reconstruction techniques make the hidden states explicit, reducing temporal learning to a feed-forward problem. In short, we propose viewing iterated prediction [LF88] as the best way of training recurrent networks on deterministic signals. Using this framework, we can train multiple trajectories, insure their stabil(cid: 173) ity, and design arbitrary dynamical systems.

Authors

• Fu-Sheng Tsung
• Garrison Cottrell

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