non-linear autoregressive neural networks with external inputs (narx) networks Search Results


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MLX90614 temperature setup over the <t>SMA</t> <t>wire.</t>
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Image Search Results


MLX90614 temperature setup over the SMA wire.

Journal: Frontiers in Robotics and AI

Article Title: Two-Stage Shape Memory Alloy Identification Based on the Hammerstein–Wiener Model

doi: 10.3389/frobt.2019.00083

Figure Lengend Snippet: MLX90614 temperature setup over the SMA wire.

Article Snippet: According to the SMA wire behavior (presenting non-linearities) different non-linear models from System identification toolbox of Matlab, such as NARX and Hammerstein–Wiener have been pre–tested.

Techniques:

Measured and simulated model of the SMA wire.

Journal: Frontiers in Robotics and AI

Article Title: Two-Stage Shape Memory Alloy Identification Based on the Hammerstein–Wiener Model

doi: 10.3389/frobt.2019.00083

Figure Lengend Snippet: Measured and simulated model of the SMA wire.

Article Snippet: According to the SMA wire behavior (presenting non-linearities) different non-linear models from System identification toolbox of Matlab, such as NARX and Hammerstein–Wiener have been pre–tested.

Techniques:

Error area between simulated model and real SMA wire.

Journal: Frontiers in Robotics and AI

Article Title: Two-Stage Shape Memory Alloy Identification Based on the Hammerstein–Wiener Model

doi: 10.3389/frobt.2019.00083

Figure Lengend Snippet: Error area between simulated model and real SMA wire.

Article Snippet: According to the SMA wire behavior (presenting non-linearities) different non-linear models from System identification toolbox of Matlab, such as NARX and Hammerstein–Wiener have been pre–tested.

Techniques:

Control algorithm test for the real and simulated model of the SMA wire.

Journal: Frontiers in Robotics and AI

Article Title: Two-Stage Shape Memory Alloy Identification Based on the Hammerstein–Wiener Model

doi: 10.3389/frobt.2019.00083

Figure Lengend Snippet: Control algorithm test for the real and simulated model of the SMA wire.

Article Snippet: According to the SMA wire behavior (presenting non-linearities) different non-linear models from System identification toolbox of Matlab, such as NARX and Hammerstein–Wiener have been pre–tested.

Techniques: Control

Real and simulated model of the SMA wire controlled in position tracking a step reference.

Journal: Frontiers in Robotics and AI

Article Title: Two-Stage Shape Memory Alloy Identification Based on the Hammerstein–Wiener Model

doi: 10.3389/frobt.2019.00083

Figure Lengend Snippet: Real and simulated model of the SMA wire controlled in position tracking a step reference.

Article Snippet: According to the SMA wire behavior (presenting non-linearities) different non-linear models from System identification toolbox of Matlab, such as NARX and Hammerstein–Wiener have been pre–tested.

Techniques:

Real and simulated model of the SMA wire controlled in position tracking a sine reference.

Journal: Frontiers in Robotics and AI

Article Title: Two-Stage Shape Memory Alloy Identification Based on the Hammerstein–Wiener Model

doi: 10.3389/frobt.2019.00083

Figure Lengend Snippet: Real and simulated model of the SMA wire controlled in position tracking a sine reference.

Article Snippet: According to the SMA wire behavior (presenting non-linearities) different non-linear models from System identification toolbox of Matlab, such as NARX and Hammerstein–Wiener have been pre–tested.

Techniques:

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: