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.
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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.
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Paperback. Condición: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781852332273
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