Name: Neural Approximations for Optimal Control and Decision (en Inglés)
SKU: 57242431
Price: 180.35 USD
Availability: InStock
Author: Zoppoli, Riccardo ; Sanguineti, Marcello ; Gnecco, Giorgio
ISBN: 9783030296919

portada Neural Approximations for Optimal Control and Decision (en Inglés)

Neural Approximations for Optimal Control and Decision (en Inglés)

Formato

Libro Físico

Autor

Editorial

Idioma

Inglés

N° páginas

517

Encuadernación

Tapa Dura

Dimensiones

23.9x16.3x2.5 cm

Peso

1.09 kg.

ISBN13

9783030296919

Categorías

Cibernética y teoría de sistemas
Investigación operativa
Ingeniería de control automático

Neural Approximations for Optimal Control and Decision (en Inglés)

Riccardo Zoppoli (Autor) · Marcello Sanguineti (Autor) · Giorgio Gnecco (Autor) · Springer · Tapa Dura

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Reseña del libro "Neural Approximations for Optimal Control and Decision (en Inglés)"

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 onedecision 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.