Click Here to Download: https://ouo.io/dVbtSZ Neural Approximations for Optimal Control and Decision By: Riccardo Zoppoli; Marcello Sanguineti; Giorgio Gnecco; Thomas Parisini Publisher: Springer Print ISBN: 9783030296919, 3030296911 eText ISBN: 9783030296933, 3030296938 Copyright year: 2020 Format: EPUB Available from $ 209.00 USD SKU 9783030296933 Neural Approximations for Optimal Control and Decision provides a comprehensive methodology for the approximate solution of functional optimization problems using neural networks and other nonlinear approximators where the use of traditional optimal control tools is prohibited by complicating factors like non-Gaussian noise, strong nonlinearities, large dimension of state and control vectors, etc. Features of the text include: • a general functional optimization framework; • thorough illustration of recent theoretical insights into the approximate solutions of complex functional optimization problems; • comparison of classical and neural-network based methods of approximate solution; • bounds to the errors of approximate solutions; • solution algorithms for optimal control and decision in deterministic or stochastic environments with perfect or imperfect state measurements over a finite or infinite time horizon and with one decision maker or several; • applications of current interest: routing in communications networks, traffic control, water resource management, etc.; and • numerous, numerically detailed examples. The authors’ diverse backgrounds in systems and control theory, approximation theory, machine learning, and operations research lend the book a range of expertise and subject matter appealing to academics and graduate students in any of those disciplines together with computer science and other areas of engineering.
