A state-of-the-art research monograph providing consistent treatment of supervisory control an area which has seen little thorough and consistent theoretical framework coverage. Additional added value of the book arises from scientific treatment of the role of the operator in process supervision, thus making a significant contribution to the design of anthropocentric automation systems.

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

Klappentext

Written by one of the world's leading groups in the area of Bayesian identification, control and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising.
Starting from abstract ideas and formulations, and culminating in detailed algorithms, Optimized Bayesian Dynamic Advising comprises a unified treatment of an important problem of the design of advisory systems supporting supervisors of complex processes. It introduces the theoretical and algorithmic basis of developed advising, relying on novel and powerful combination black-box modeling by dynamic mixture models and fully probabilistic dynamic optimization. The proposed non-standard problem formulation and its solution mark a significant contribution to the design of anthropocentric automation systems.
Written for a broad audience, including developers of algorithms and application engineers, researchers, lecturers and postgraduates, this book can be used as a reference tool, and an advanced text on Bayesian dynamic decision making.

Inhalt
Underlying theory.- Approximate and feasible learning.- Approximate design.- Problem formulation.- Solution and principles of its approximation: learning part.- Solution and principles of its approximation: design part.- Learning with normal factors and components.- Design with normal mixtures.- Learning with Markov-chain factors and components.- Design with Markov-chain mixtures.- Sandwich BMTB for mixture initiation.- Mixed mixtures.- Applications of the advisory system.- Concluding remarks.