A Jordan-type LSTM that feeds the previous state estimate back into the network is shown to match Elman LSTM accuracy while training faster on three state estimation examples.
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State estimator design using Jordan based long short-term memory networks
A Jordan-type LSTM that feeds the previous state estimate back into the network is shown to match Elman LSTM accuracy while training faster on three state estimation examples.