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ニューラル状態空間モデル

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。

ライブ エディター タスク

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)

関数

createMLPNetworkCreate and initialize a Multi-Layer Perceptron (MPL) 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)
sim同定されたモデルの応答のシミュレーション

オブジェクト

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)

ブロック

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

トピック

  • 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 aftertreatment 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.