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Neural Approximations for Optimal Control and Decision by Riccardo Zoppoli (Engl
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Located in: Calgary, Alberta, Canada
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eBay item number:364800616735
Item specifics
- Condition
- Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
- ISBN-13
- 9783030296919
- Type
- NA
- Publication Name
- NA
- ISBN
- 9783030296919
- Publication Year
- 2019
- Format
- Hardcover
- Language
- English
- Book Title
- Neural Approximations for Optimal Control and Decision
- Item Length
- 9.3in
- Publisher
- Springer International Publishing A&G
- Genre
- Technology & Engineering, Science, Business & Economics
- Topic
- Operations Research, System Theory, Electrical
- Item Width
- 6.1in
- Item Weight
- 42.5 Oz
- Number of Pages
- Xviii, 517 Pages
About this product
Product Information
Many areas of science and technology require the solution of functional optimization problems, that is, problems with feasible solutions belonging to infinite-dimensional spaces. This is the case, for example in stochastic optimal control of communication or traffic networks: large organizations in which many individual decision makers, each with different information available cooperate for the accomplishment of a common goal. In such circumstances, there is often a variety of technical impediments to the use of traditional optimal control tools - strong nonlinearity, non-Gaussian noise, multiple decision makers, "Bellman's curse of dimensionality", and so on. Neural Approximations for Optimal Control and Decision propounds a method of constraining the admissible control or decision functions to take on the structure of neural networks or other nonlinear approximators in which a certain number of parameters must be optimized: the "Extended Ritz Method" or ERIM. Using the ERIM, functional optimization problems are reduced to matters of nonlinear programming. By combining ideas drawn from functional optimization, optimal control, nonlinear approximation and data-based learning, computationally efficient approximation schemes are derived. Such schemes are expressible as combinations of simple computational units dependent on parameters like the weights in neural networks and are optimized with nonlinear programming algorithms. Features of the text include: * an overview of classical computational methods: discrete dynamic programming, gradient techniques, the Ritz method etc.; * a thorough illustration of recent theoretical insights into the approximate solutions of complex functional optimization problems; * an organic comparison of classical and neural-network-based methods of approximate solution; * a derivation of the theoretical properties of the ERIM, a novel methodology of functional optimization based on nonlinear approximators; * bounds to the errors of approximate solutions; * efficient solution algorithms for a range of problems: 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 to major fields of current interest: routing in communications networks, freeway traffic control, water resource management, stochastic shortest paths, exploration of unknown environments, etc.; * numerous examples - often dealing with real applications and developed in full numerical detail. The authors' diverse backgrounds in automatic control, systems theory and operations research lend the book a range of expertise and subject matter appealing to academics and graduate students in all those disciplines together with computer science and other areas of engineering.
Product Identifiers
Publisher
Springer International Publishing A&G
ISBN-10
3030296911
ISBN-13
9783030296919
eBay Product ID (ePID)
2309606684
Product Key Features
Book Title
Neural Approximations for Optimal Control and Decision
Format
Hardcover
Language
English
Topic
Operations Research, System Theory, Electrical
Publication Year
2019
Genre
Technology & Engineering, Science, Business & Economics
Number of Pages
Xviii, 517 Pages
Dimensions
Item Length
9.3in
Item Width
6.1in
Item Weight
42.5 Oz
Additional Product Features
Number of Volumes
1 Vol.
Lc Classification Number
Tj212-225
Table of Content
Chapter 1. The Basic InFinite-Dimensional or Functional Optimization Problem.- Chapter 2. From Functional Optimization to Nonlinear Programming by the Extended Ritz Method.- Chapter 3. Some Families of FSP Functions and Their Properties.- Chapter 4. Design of Mathematical Models by Learning from Data and FSP Functions.- Chapter 5. Numerical Methods for Integration and Search for Minima.- Chapter 6. Deterministic Optimal Control Over a Finite Horizon.- Chapter 7. Stochastic Optimal Control with Perfect State Information over a Finite Horizon.- Chapter 8. Stochastic Optimal Control with Imperfect State Information over a Finite Horizon.- Chapter 9. Team Optimal Control Problems.- Chapter 10. Optimal Control Problems over an InFinite Horizon.- Index.
Copyright Date
2020
Series
Communications and Control Engineering Ser.
Illustrated
Yes
Item description from the seller
Seller assumes all responsibility for this listing.
eBay item number:364800616735
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Calgary, Alberta, Canada
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