About this product

Product Identifiers

PublisherSpringer London, The Limited

ISBN-101852332271

ISBN-139781852332273

eBay Product ID (ePID)1881765

Product Key Features

Number of PagesXiv, 246 Pages

LanguageEnglish

Publication NameNeural Networks for Modelling and Control of Dynamic Systems : a Practitioner's Handbook

Publication Year2000

SubjectAutomation, Computer Simulation, Neural Networks, Electrical, System Theory, Mathematical Analysis

TypeTextbook

Subject AreaMathematics, Computers, Technology & Engineering, Science

AuthorL. K. Hansen, Magnus Norgaard, N. K. Poulsen, O. E. Ravn

SeriesAdvanced Textbooks in Control and Signal Processing Ser.

FormatTrade Paperback

Dimensions

Item Height0.2 in

Item Weight29.3 Oz

Item Length9.3 in

Item Width6.1 in

Additional Product Features

Intended AudienceScholarly & Professional

LCCN99-049801

Dewey Edition21

Number of Volumes1 vol.

IllustratedYes

Dewey Decimal006.3/2

Table Of Content1. Introduction.- 1.1 Background.- 1.2 Introduction to Multilayer Perceptron Networks.- 2. System Identification with Neural Networks.- 2.1 Introduction to System Identification.- 2.2 Model Structure Selection.- 2.3 Experiment.- 2.4 Determination of the Weights.- 2.5 Validation.- 2.6 Going Backwards in the Procedure.- 2.7 Recapitulation of System Identification.- 3. Control with Neural Networks.- 3.1 Introduction to Neural-Network-based Control.- 3.2 Direct Inverse Control.- 3.3 Internal Model Control (IMC).- 3.4 Feedback Linearization.- 3.5 Feedforward Control.- 3.6 Optimal Control.- 3.7 Controllers Based on Instantaneous Linearization.- 3.8 Predictive Control.- 3.9 Recapitulation of Control Design Methods.- 4. Case Studies.- 4.1 The Sunspot Benchmark.- 4.2 Modelling of a Hydraulic Actuator.- 4.3 Pneumatic Servomechanism.- 4.4 Control of Water Level in a Conic Tank.- References.

SynopsisThe 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., The technology of neural networks has attracted much attention in recentyears. Their ability to learn nonlinear relationships is widelyappreciated and is utilized in many different types of applications;modelling of dynamic systems, signal processing, and control system designbeing some of the most common. The theory of neural computing has maturedconsiderably over the last decade and many problems of neural networkdesign, training and evaluation have been resolved. This book provides acomprehensive introduction to the most popular class of neural network,the multilayer perceptron, and shows how it can be used for systemidentification and control. It aims to provide the reader with asufficient theoretical background to understand the characteristics ofdifferent methods, to be aware of the pit-falls and to make properdecisions in all situations. The subjects treated include:System identification: multilayer perceptrons; how to conduct informativeexperiments; model structure selection; training methods; modelvalidation; pruning algorithms.Control: direct inverse, internal model, feedforward, optimal andpredictive control; feedback linearization andinstantaneous-linearization-based controllers.Case studies: prediction of sunspot activity; modelling of a hydraulicactuator; control of a pneumatic servomechanism; water-level control in aconical tank.The book is very application-oriented and gives detailed and pragmaticrecommendations that guide the user through the plethora of methodssuggested in the literature. Furthermore, it attempts to introduce soundworking procedures that can lead to efficient neural network solutions.This will make the book invaluable to the practitioner and as a textbookin courses with a significant hands-on component., A comprehensive introduction to the most popular class of neural network, the multilayer perceptron, showing how it can be used for system identification and control. The book provides readers with a sufficient theoretical background to understand the characteristics of different methods, and to be aware of the pit-falls so as to make the correct decisions in all situations. This is a very application-oriented text that gives detailed and pragmatic recommendations to guide users through the plethora of methods suggested in the literature. Furthermore, it introduces sound working procedures that can lead to efficient neural network solutions. Invaluable to the practitioner and as a textbook in courses with a significant hands-on component.

LC Classification NumberTJ212-225