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Neural Approximations for Optimal Control and Decision

  • Riccardo Zoppoli
  • Marcello Sanguineti
  • Giorgio Gnecco
  • Thomas Parisini
Book

Part of the Communications and Control Engineering book series (CCE)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Riccardo Zoppoli, Marcello Sanguineti, Giorgio Gnecco, Thomas Parisini
    Pages 1-38
  3. Riccardo Zoppoli, Marcello Sanguineti, Giorgio Gnecco, Thomas Parisini
    Pages 39-88
  4. Riccardo Zoppoli, Marcello Sanguineti, Giorgio Gnecco, Thomas Parisini
    Pages 89-150
  5. Riccardo Zoppoli, Marcello Sanguineti, Giorgio Gnecco, Thomas Parisini
    Pages 151-206
  6. Riccardo Zoppoli, Marcello Sanguineti, Giorgio Gnecco, Thomas Parisini
    Pages 207-253
  7. Riccardo Zoppoli, Marcello Sanguineti, Giorgio Gnecco, Thomas Parisini
    Pages 255-298
  8. Riccardo Zoppoli, Marcello Sanguineti, Giorgio Gnecco, Thomas Parisini
    Pages 299-382
  9. Riccardo Zoppoli, Marcello Sanguineti, Giorgio Gnecco, Thomas Parisini
    Pages 383-426
  10. Riccardo Zoppoli, Marcello Sanguineti, Giorgio Gnecco, Thomas Parisini
    Pages 427-469
  11. Riccardo Zoppoli, Marcello Sanguineti, Giorgio Gnecco, Thomas Parisini
    Pages 471-511
  12. Back Matter
    Pages 513-517

About this book

Introduction

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.

Keywords

Bellman's Curse of Dimensionality Control Control Engineering Control Theory Decision Engineering Neural Networks Nonlinear Control Nonlinear Systems Optimal Control Optimization Ritz Method

Authors and affiliations

  1. 1.DIBRISUniversità di GenovaGenovaItaly
  2. 2.DIBRISUniversità di GenovaGenovaItaly
  3. 3.AXES Research UnitIMT—School of Advanced Studies LuccaLuccaItaly
  4. 4.Imperial College LondonLondonUK

Bibliographic information