
Deterministic Learning Theory for Identification, Recognition, and Control
For Identiflcation, Recognition, and Conirol
CRC Press
1st Edition
Published on 6. October 2017
Book
Paperback/Softback
207 pages
978-1-138-11205-6 (ISBN)
Description
Deterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation, and knowledge utilization in uncertain dynamic environments. It provides systematic design approaches for identification, recognition, and control of linear uncertain systems. Unlike many books currently available that focus on statistical principles, this book stresses learning through closed-loop neural control, effective representation and recognition of temporal patterns in a deterministic way.
A Deterministic View of Learning in Dynamic Environments
The authors begin with an introduction to the concepts of deterministic learning theory, followed by a discussion of the persistent excitation property of RBF networks. They describe the elements of deterministic learning, and address dynamical pattern recognition and pattern-based control processes. The results are applicable to areas such as detection and isolation of oscillation faults, ECG/EEG pattern recognition, robot learning and control, and security analysis and control of power systems.
A New Model of Information Processing
This book elucidates a learning theory which is developed using concepts and tools from the discipline of systems and control. Fundamental knowledge about system dynamics is obtained from dynamical processes, and is then utilized to achieve rapid recognition of dynamical patterns and pattern-based closed-loop control via the so-called internal and dynamical matching of system dynamics. This actually represents a new model of information processing, i.e. a model of dynamical parallel distributed processing (DPDP).
A Deterministic View of Learning in Dynamic Environments
The authors begin with an introduction to the concepts of deterministic learning theory, followed by a discussion of the persistent excitation property of RBF networks. They describe the elements of deterministic learning, and address dynamical pattern recognition and pattern-based control processes. The results are applicable to areas such as detection and isolation of oscillation faults, ECG/EEG pattern recognition, robot learning and control, and security analysis and control of power systems.
A New Model of Information Processing
This book elucidates a learning theory which is developed using concepts and tools from the discipline of systems and control. Fundamental knowledge about system dynamics is obtained from dynamical processes, and is then utilized to achieve rapid recognition of dynamical patterns and pattern-based closed-loop control via the so-called internal and dynamical matching of system dynamics. This actually represents a new model of information processing, i.e. a model of dynamical parallel distributed processing (DPDP).
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Professional
Illustrations
147 s/w Abbildungen
147 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 156 mm
Weight
453 gr
ISBN-13
978-1-138-11205-6 (9781138112056)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Cong Wang | David J. Hill
Deterministic Learning Theory for Identification, Recognition, and Control
E-Book
10/2018
1st Edition
CRC Press
€68.49
Available for download

Cong Wang | David J. Hill
Deterministic Learning Theory for Identification, Recognition, and Control
E-Book
10/2018
1st Edition
CRC Press
€68.49
Available for download

Cong Wang | David J. Hill
Deterministic Learning Theory for Identification, Recognition, and Control
For Identiflcation, Recognition, and Conirol
Book
07/2009
1st Edition
CRC Press
€277.10
Article not available at the moment
Persons
Cong Wang, David J. Hill
Content
Introduction. RBF Networks and the PE Condition. Locally Accurate Identification of Nonlinear Systems. Learning from Closed-Loop Neural Control. Rapid Recognition of Dynamical Patterns. Deterministic Learning using Output Measurements. Applications of Deterministic Learning. Conclusions.