Deterministic Learning Theory for Identification, Recognition, and Control - Automation and Control Engineering (Hardback)
  • Deterministic Learning Theory for Identification, Recognition, and Control - Automation and Control Engineering (Hardback)
zoom

Deterministic Learning Theory for Identification, Recognition, and Control - Automation and Control Engineering (Hardback)

(author), (author)
£165.00
Hardback 207 Pages / Published: 21/07/2009
  • We can order this

Usually dispatched within 3 weeks

  • This item has been added to your basket

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).

Publisher: Taylor & Francis Inc
ISBN: 9780849375538
Number of pages: 207
Weight: 499 g
Dimensions: 235 x 156 x 20 mm

You may also be interested in...

An Introduction to Genetic Algorithms
Added to basket
Not Exactly
Added to basket
£16.99
Hardback
Expert Systems Lab Course
Added to basket
Patterns in Java
Added to basket
£29.99
Paperback
Superintelligence
Added to basket
£18.99
Hardback
Boosting
Added to basket
£32.00
Paperback
Machine Learning for Hackers
Added to basket
The Sciences of the Artificial
Added to basket
Programming Computer Vision with Python
Added to basket
Emotion: A Very Short Introduction
Added to basket
Introducing Artificial Intelligence
Added to basket
Multi-Agent Machine Learning
Added to basket

Please sign in to write a review

Your review has been submitted successfully.