Overview
- Provides a generic methodology with elaborated algorithmic image of probabilistic, possibly adaptive, optimised advisory system supporting dynamic decision making under uncertainty in a complex environment
- Dynamic, adaptive, mixture modelling of non-linear uncertain systems from le6 data records, each having several tens of entries, has not been done before
- Optimization of advises in a fully probabilistic sense has not been done before
- Brings a completely new treatment of the topic of supervisory control of nonlinear uncertain systems to the fore
- Neither book nor solution, have a viable competitor
- Original problem formulation and practical solution of the optimised and adaptive advising
- Many particular, often novel, results widely applicable in signal processing, modelling and estimation of non-linear systems, multi-step prediction, pattern recognition and (adaptive) control
- Diverse application potential from technological processes, medical diagnostics, control of urban traffic to economical and societal processes
- Includes supplementary material: sn.pub/extras
Part of the book series: Advanced Information and Knowledge Processing (AI&KP)
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Table of contents (15 chapters)
Keywords
About this book
Editors and Affiliations
Bibliographic Information
Book Title: Optimized Bayesian Dynamic Advising
Book Subtitle: Theory and Algorithms
Editors: Miroslav Karny
Series Title: Advanced Information and Knowledge Processing
DOI: https://doi.org/10.1007/1-84628-254-3
Publisher: Springer London
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag London 2006
Hardcover ISBN: 978-1-85233-928-9Published: 10 October 2005
Softcover ISBN: 978-1-4471-5675-8Published: 20 October 2014
eBook ISBN: 978-1-84628-254-6Published: 19 December 2005
Series ISSN: 1610-3947
Series E-ISSN: 2197-8441
Edition Number: 1
Number of Pages: XVII, 529
Topics: Models and Principles, User Interfaces and Human Computer Interaction, Artificial Intelligence, Simulation and Modeling, Pattern Recognition, Statistics and Computing/Statistics Programs