A new mathematical representation, based on a discrete-time nonlinear state space formulation, is presented to characterize a Generalized Auto Regresive Conditional Heteroskedasticity (GARCH) model. Nonlinear parameter ...

Model predictive control (MPC) has become very popular in process industry and academia because it is an optimizing control technique which can handle hard constraints as well as time delays and mild nonlinearities. Linear ...

The robust stability and convergence to the true state of a moving horizon estimator based on an adaptive arrival cost are established for non-linear detectable systems in this study. Robust global asymptotic stability is ...

This paper deals with the problem of nonlinear states estimation in batch chemical processes. It presents a reduced-order nonlinear observer approach to perform the estimation. The proposed method allows adjustment of the ...

The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel barrier method using artificial neural networks to solve robust parameter ...

The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel barrier method using artificial neural networks to solve robust parameter ...

Attempts are being made to improve mechanical design by using nonlinearity rather than eliminating it, especially in the area of vibration control and in energy harvesting. In such systems, there is a need to both predict ...

We prove sharp regularity estimates for viscosity solutions of fully nonlinear parabolic equations of the form (Formula Presented.)where F is elliptic with respect to the Hessian argument and f∈ Lp , q(Q1). The quantity ...