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。

関数

`createMLPNetwork`	Create and initialize a Multi-Layer Perceptron (MPL) network to be used within a neural state-space system
`nssTrainingOptions`	Create training options object for neural state-space systems
`nlssest`	Estimate nonlinear state-space model using measured time-domain system data
`generateMATLABFunction`	Generate MATLAB functions that evaluate the state and output functions of a neural state-space object, and their Jacobians
`idNeuralStateSpace/evaluate`	Evaluate a neural state-space system for a given set of state and input values and return state derivative (or next state) and output values
`idNeuralStateSpace/linearize`	Linearize a neural state-space model around an operating point
`sim`	Simulate response of identified model

`idNeuralStateSpace`	Neural state-space model with identifiable network weights
`nssTrainingADAM`	Adam training options object for neural state-space systems
`nssTrainingSGDM`	SGDM training options object for neural state-space systems

Simulate neural state-space model in Simulink

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.