Sentence examples for modeling multivariable systems from inspiring English sources

In addition to modeling multivariable systems, a Bayesian network provides a natural platform for incorporating prior biological knowledge.

Neural networks have a potential for intelligent control systems because they can learn and adapt, they can approximate nonlinear functions, they are suited for parallel and distributed processing, and they naturally model multivariable systems.

Model inversion in multivariable systems is non-trivial.

In this paper, the inverters with different characteristics in a microgrid are modeled as a multivariable system.

The design of high performance model-predictive controllers for multivariable systems having both time delays and right-half-plane (RHP) zeros is a challenge.

This paper develops an on-line model parameter identification approach for multivariable systems, which are nonlinear in terms of state representation and/or in terms of parameters.

In this paper, we develop a hybrid design framework of model predictive controller (MPC) for multivariable systems that simultaneously and explicitly addresses the actuator saturation and backlash.

It is based on the identification of a state space model of the multivariable system, and it uses constrained nonlinear optimization techniques to find the controller parameters.

It requires the identification of a full dynamic model of the multivariable system, and uses constrained nonlinear optimization techniques to find the controller parameters.

This paper presents a free model reduction closed loop subspace identification method for multivariable systems operating in a well posed closed loop environment.

The first step in subspace methods for identification of multivariable systems is the estimation of the order of the model to be identified.