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ニューラル ネットワークを使用して、システムの非線形状態空間実現を定義する関数を表します。

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ニューラル状態空間モデルの推定Estimate neural state-space model in the Live Editor (R2023b 以降)


createMLPNetworkCreate and initialize a Multi-Layer Perceptron (MLP) network to be used within a neural state-space system (R2022b 以降)
nssTrainingOptionsCreate training options object for neural state-space systems (R2022b 以降)
nlssestEstimate nonlinear state-space model using measured time-domain system data (R2022b 以降)
generateMATLABFunctionGenerate MATLAB functions that evaluate the state and output functions of a neural state-space object, and their Jacobians (R2022b 以降)
idNeuralStateSpace/evaluateEvaluate a neural state-space system for a given set of state and input values and return state derivative (or next state) and output values (R2022b 以降)
idNeuralStateSpace/linearizeLinearize a neural state-space model around an operating point (R2022b 以降)


idNeuralStateSpaceNeural state-space model with identifiable network weights (R2022b 以降)
nssTrainingADAMAdam training options object for neural state-space systems (R2022b 以降)
nssTrainingSGDMSGDM training options object for neural state-space systems (R2022b 以降)


Neural State-Space ModelSimulate neural state-space model in Simulink (R2022b 以降)


  • About Identified Nonlinear Models

    Dynamic models in System Identification Toolbox™ software are mathematical relationships between the inputs u(t) and outputs y(t) of a system. The model is dynamic because the output value at the current time depends on the input-output values at previous time instants. Therefore, dynamic models have memory of the past. You can use the input-output relationships to compute the current output from previous inputs and outputs. Dynamic models have states, where a state vector contains the information of the past.

  • Neural State-Space Model of SI Engine Torque Dynamics

    This example describes reduced order modeling (ROM) of the nonlinear torque dynamics of a spark-ignition (SI) engine using a neural state-space model. The identified model can be used for hardware-in-the-loop (HIL) testing, powertrain control, diagnostics, and training algorithm design. For example, you can use the model for after-treatment control and diagnostic algorithm development. For more information on neural state-space models, see Neural State-Space Models.

  • Neural State-Space Model of Simple Pendulum System

    This example shows how to design and train a deep neural network that approximates a nonlinear state-space system in continuous time.

  • Reduced Order Modeling of Electric Vehicle Battery System Using Neural State-Space Model

    This example shows a reduced order modeling (ROM) workflow, where you use deep learning to obtain a low-order nonlinear state-space model that serves as a surrogate for a high-fidelity battery model. The low-order model takes the current (charge or discharge) and state of charge (SOC) as inputs and predicts voltage and temperature of an electric vehicle (EV) battery module while the battery is being cooled by an edge-cooled plate with a coolant at a constant flow rate. You train the low-order model and deploy it in Simulink® to compare it against the high-fidelity model.