Del i serien Control, Robotics and Sensors

Intelligent Control Systems using Computational Intelligence Techniques

Inbunden, Engelska, 2005

1 979 kr

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Intelligent Control techniques are becoming important tools in both academia and industry. Methodologies developed in the field of soft-computing, such as neural networks, fuzzy systems and evolutionary computation, can lead to accommodation of more complex processes, improved performance and considerable time savings and cost reductions. Intelligent Control Systems using Computational Intellingence Techniques details the application of these tools to the field of control systems. Each chapter gives and overview of current approaches in the topic covered, with a set of the most important references in the field, and then details the author's approach, examining both the theory and practical applications.

Produktinformation

Utgivningsdatum2005-07-01
Mått156 x 234 x 30 mm
Vikt816 g
FormatInbunden
SpråkEngelska
SerieControl, Robotics and Sensors
Antal sidor476
FörlagInstitution of Engineering and Technology
ISBN9780863414893

Tillhör följande kategorier

Elektronik och kommunikationer inom Naturvetenskap och teknik

Antonio Ruano received his First Degree in Electronic and Telecommunications Engineering from the University of Aveiro, Portugal, in 1982, his MSc in Electrothecnic Engineering from the University of Wales in 1992. In 1992 he joined the Department of Electronic Engineering and Informatics of the University of Algarve, where in 1996 he became Associate Professor of Automatic Control. He is Associate Editor for Automatica, a member of the Editorial Board of International Journal of Systems Science, and serves a reviewer for several journals and international conferences. He is a senior member of the IEE and a member of the Cognition for Control, Real-Time Computing and Control and Computer Control for Agricultural Applications TCs of IFAC.

Chapter 1: An overview of nonlinear identification and control with fuzzy systemsChapter 2: An overview of nonlinear identification and control with neural networksChapter 3: Multi-objective evolutionary computing solutions for control and system identificationChapter 4: Adaptive local linear modelling and control of nonlinear dynamical systemsChapter 5: Nonlinear system identification with local linear neuro-fuzzy modelsChapter 6: Gaussian process approaches to nonlinear modelling for controlChapter 7: Neuro-fuzzy model construction, design and estimationChapter 8: A neural network approach for nearly optimal control of constrained nonlinear systemsChapter 9: Reinforcement learning for online control and optimisationChapter 10: Reinforcement learning and multi-agent control within an internet environmentChapter 11: Combined computational intelligence and analytical methods in fault diagnosisChapter 12: Application of intelligent control to autonomous search of parking place and parking of vehiclesChapter 13: Applications of intelligent control in medicine

'a very comprehensive and up-to-date treatise of this important and fascinating field. The book is an excellent read for anyone (academic or industrialist) who wishes to discover how to utilise computational intelligence to solve realistic problems. It is an extremely interesting and fascinating book on this dynamic subject, I certainly found it an enthralling read and it has been given a prominent place on my bookshelf.'