Delay Embedded Echo-State Network: A Predictor for Partially Observed Systems

• URL: http://arxiv.org/abs/2211.05992v2
• Date: Wed, 5 Apr 2023 22:40:17 GMT
• Title: Delay Embedded Echo-State Network: A Predictor for Partially Observed Systems
• Authors: Debdipta Goswami
• Abstract summary: A predictor for partial observations is developed using an echo-state network (ESN) and time delay embedding of the partially observed state. The proposed method is theoretically justified with Taken's embedding theorem and strong observability of a nonlinear system.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper considers the problem of data-driven prediction of partially observed systems using a recurrent neural network. While neural network based dynamic predictors perform well with full-state training data, prediction with partial observation during training phase poses a significant challenge. Here a predictor for partial observations is developed using an echo-state network (ESN) and time delay embedding of the partially observed state. The proposed method is theoretically justified with Taken's embedding theorem and strong observability of a nonlinear system. The efficacy of the proposed method is demonstrated on three systems: two synthetic datasets from chaotic dynamical systems and a set of real-time traffic data.

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