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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。


createMLPNetworkCreate and initialize a Multi-Layer Perceptron (MPL) network to be used within a neural state-space system
nssTrainingOptionsCreate training options object for neural state-space systems
nlssestEstimate nonlinear state-space model using measured time-domain system data
generateMATLABFunctionGenerate MATLAB functions that evaluate the state and output functions of a neural state-space object, and their Jacobians
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
idNeuralStateSpace/linearizeLinearize a neural state-space model around an operating point
simSimulate response of identified model


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


Neural State-Space ModelSimulate 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.