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.