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In mathematics and science, a nonlinear system (or a non-linear system) is a system in which the change of the output is not proportional to the change of the input. [1] [2] Nonlinear problems are of interest to engineers, biologists, [3] [4] [5] physicists, [6] [7] mathematicians, and many other scientists since most systems are inherently nonlinear in nature. [8]
System identification is a method of identifying or measuring the mathematical model of a system from measurements of the system inputs and outputs. The applications of system identification include any system where the inputs and outputs can be measured and include industrial processes, control systems, economic data, biology and the life sciences, medicine, social systems and many more.
In estimation theory, the extended Kalman filter (EKF) is the nonlinear version of the Kalman filter which linearizes about an estimate of the current mean and covariance.In the case of well defined transition models, the EKF has been considered [1] the de facto standard in the theory of nonlinear state estimation, navigation systems and GPS.
When the state transition and observation models—that is, the predict and update functions and —are highly nonlinear, the extended Kalman filter can give particularly poor performance. [ 65 ] [ 66 ] This is because the covariance is propagated through linearization of the underlying nonlinear model.
then under some regularity assumptions the density () satisfies a non-linear stochastic partial differential equation (SPDE) driven by and called Kushner-Stratonovich equation, [10] or a unnormalized version () of the density () satisfies a linear SPDE called Zakai equation. [10] These equations can be formulated for the above system, but to ...
The internal state variables are the smallest possible subset of system variables that can represent the entire state of the system at any given time. [13] The minimum number of state variables required to represent a given system, , is usually equal to the order of the system's defining differential equation, but not necessarily.
Non-linear least squares is the form of least squares analysis used to fit a set of m observations with a model that is non-linear in n unknown parameters (m ≥ n). It is used in some forms of nonlinear regression. The basis of the method is to approximate the model by a linear one and to refine the parameters by successive iterations.
An example of a nonlinear control system is a thermostat-controlled heating system. A building heating system such as a furnace has a nonlinear response to changes in temperature; it is either "on" or "off", it does not have the fine control in response to temperature differences that a proportional (linear) device would have.