
Neural Networks for Modelling and Control of Dynamic Systems by M Norgaard
The technology of neural networks has attracted much attention in recent years. Their ability to learn nonlinear relationships is widely appreciated and is utilized in many different types of applications; modelling of dynamic systems, signal processing, and control system design being some of the most common. The theory of neural computing has matured considerably over the last decade and many problems of neural network design, training and evaluation have been resolved. This book provides a comprehensive introduction to the most popular class of neural network, the multilayer perceptron, and shows how it can be used for system identification and control. It aims to provide the reader with a sufficient theoretical background to understand the characteristics of different methods, to be aware of the pit-falls and to make proper decisions in all situations. The subjects treated include: System identification: multilayer perceptrons; how to conduct informative experiments; model structure selection; training methods; model validation; pruning algorithms. Control: direct inverse, internal model, feedforward, optimal and predictive control; feedback linearization and instantaneous-linearization-based controllers. Case studies: prediction of sunspot activity; modelling of a hydraulic actuator; control of a pneumatic servomechanism; water-level control in a conical tank. The book is very application-oriented and gives detailed and pragmatic recommendations that guide the user through the plethora of methods suggested in the literature. Furthermore, it attempts to introduce sound working procedures that can lead to efficient neural network solutions. This will make the book invaluable to the practitioner and as a textbook in courses with a significant hands-on component.-
Robotics
-
Robust Control Design with MATLAB (R)
-
Control Engineering
-
Lectures in Feedback Design for Multivariable Systems
-
Foundations of Robotics
-
Digital Self-tuning Controllers
-
Statistical Signal Processing
-
Discrete-time Stochastic Systems
-
Principles of Adaptive Filters and Self-learning Systems
-
Soft Computing
-
Control of Dead-time Processes
-
Model-Reference Adaptive Control
-
Analysis and Design of Control Laws for Advanced Driver-Assistance Systems
-
Fault Detection and Diagnosis in Industrial Systems
-
Hybrid Dynamical Systems
-
Genetic Algorithms
-
Discrete-time Signal Processing
-
Analysis and Control of Nonlinear Process Systems
-
Control of Robot Manipulators in Joint Space
-
Introduction to Optimal Estimation
-
Automatic Control with Experiments
-
Model Predictive Control
-
Control Engineering: MATLAB Exercises
-
Stabilization and Regulation of Nonlinear Systems
-
Receding Horizon Control
-
Control Systems with Input and Output Constraints
-
Modeling and Control of Discrete-event Dynamic Systems
-
Multivariable Control Systems
-
System Identification
-
Power Electronic Converters Modeling and Control
| SKU | Unavailable |
| ISBN 13 | 9781852332273 |
| ISBN 10 | 1852332271 |
| Title | Neural Networks for Modelling and Control of Dynamic Systems |
| Author | M Norgaard |
| Series | Advanced Textbooks In Control And Signal Processing |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer London Ltd |
| Year published | 2000-02-02 |
| Number of pages | 246 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |





























