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use neural networks to represent the functions defining the nonlinear state space realization of your system

functions

	create and initialize a multi-layer perceptron (mpl) network to be used within a neural state-space system
	create training options object for neural state-space systems
	estimate nonlinear state-space model using measured time-domain system data
	generate matlab functions that evaluate the state and output functions of a neural state-space object, and their jacobians
	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
	linearize a neural state-space model around an operating point
	simulate response of identified model

objects

	neural state-space model with identifiable network weights
	adam training options object for neural state-space systems
	sgdm training options object for neural state-space systems

blocks

simulate neural state-space model in simulink

topics

dynamic models in system identification toolbox™ software are mathematical relationships between the inputs u(t) and outputs y(t) of a system.
this example describes reduced order modeling (rom) of the nonlinear torque dynamics of a spark-ignition (si) engine using a neural state-space model.