Neural Approximations for Optimal Control and Decision
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Beschreibung
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.
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.
Material is an up-to-date treatment of optimal control problems which have thus far been difficult to solve Applications selected have major current interest: routing in communications networks, freeway traffic control, water resources management, etc Authors with backgrounds in automatic control, systems theory and operations research lend the book a breadth of approach which makes the methods described applicable in many circumstances
Autor*in
Riccardo Zoppoli
Themen in »Neural Approximations for Optimal Control and Decision«
Bellman's Curse of Dimensionality Control Control Engineering Control Theory Decision Engineering Neural Networks Nonlinear Control Nonlinear Systems Optimal Control Ritz Method