By Topic

Neural approximations for multistage optimal control of nonlinear stochastic systems

Sign In

Full text access may be available.

To access full text, please use your member or institutional sign in.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Parisini, T. ; Dept. of Electr., Electron. & Comput. Eng., DEEI-Univ., Trieste, Italy ; Zoppoli, R.

Two main approximations are used to solve a nonlinear-quadratic-Gaussian (LQG) optimal control problem: the control law is assigned a given structure in which a finite number of parameters have to be determined to minimize the cost function (the chosen structure is that of a multilayer feedforward neural network); and the control law is given a “limited memory”. The errors resulting front both assumptions are discussed. Simulation results show that the proposed method may constitute a simple and effective tool for solving, to a sufficient degree of accuracy, optimal control problems traditionally regarded as difficult ones

Published in:

Automatic Control, IEEE Transactions on  (Volume:41 ,  Issue: 6 )