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Data-driven modeling methods with deep learning networks.

Journal: Sensors (Basel, Switzerland)

Article Title: The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

doi: 10.3390/s21062085

Figure Lengend Snippet: Data-driven modeling methods with deep learning networks.

Article Snippet: For example, Gao used measurement data as input and the reference state as output data to train the LSTM network [ ]; Bai used the NARX (Nonlinear autoregressive with external input) network to learn the complex relationship between the measurement, state, and filter gain in nonlinear systems [ ].

Techniques: Sequencing

Data-driven model. ( a ) GRU cell; ( b ) LSTM cell; ( c ) deep learning network.

Journal: Sensors (Basel, Switzerland)

Article Title: The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

doi: 10.3390/s21062085

Figure Lengend Snippet: Data-driven model. ( a ) GRU cell; ( b ) LSTM cell; ( c ) deep learning network.

Article Snippet: For example, Gao used measurement data as input and the reference state as output data to train the LSTM network [ ]; Bai used the NARX (Nonlinear autoregressive with external input) network to learn the complex relationship between the measurement, state, and filter gain in nonlinear systems [ ].

Techniques: