Can we cope with the curse of dimensionality in optimal control by using neural approximators?
R. Zoppoli, Marcello Sanguineti, Thomas Parisini · Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228) · 2003
An approximation procedure termed "extended Ritz method" is presented for the solution of functional optimization problems. The properties of powerful nonlinear approximators, such as neural networks, are exploited to face highly nonlinear optimization problems in high-dimensional settings, with the possibility of avoiding the so-called "curse of dimensionality." As an example, a nonlinear control problem involving several tens of state variables is faced.