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Topic: Neural Networks and Reservoir Computing ×Clear all

3 claims from 1 paper

1. Computer Science › Neural Networks and Reservoir Computing

   Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network

   Chattopadhyay, Hassanzadeh and Subramanian · Nonlinear processes in geophysics · 2020

   The paper compares three machine-learning methods for forecasting a three-tier chaotic Lorenz 96 system from the slow variable alone, judging both short-term prediction and long-term statistics.

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   1. UncheckedIn a multiscale Lorenz 96 system, an echo state network predicted short-term chaotic trajectories much better than a feed-forward network or an LSTM.“We show that RC-ESN substantially outperforms ANN and RNN-LSTM for short-term prediction, e.g., accurately forecasting the chaotic trajectories for hundreds of numerical solver's time steps, equivalent to several Lyapunov timescales.”
   2. UncheckedIn short-term forecasts of a chaotic multiscale Lorenz 96 system, an LSTM network shows some skill and does better than a feed-forward neural network.“The RNN-LSTM and ANN show some prediction skills as well; RNN-LSTM bests ANN.”
   3. UncheckedIn this Lorenz 96 test, forecasts from RC-ESN and RNN-LSTM, even after they lose the true trajectory, have value distributions matching the true one, tails included.“Furthermore, even after losing the trajectory, data predicted by RC-ESN and RNN-LSTM have probability density functions (PDFs) that closely match the true PDF, even at the tails.”