Multilayer Neural Networks: A Generalized Net Perspective: Studies in Computational Intelligence, cartea 478
Autor Maciej Krawczaken Limba Engleză Hardback – 31 mai 2013
Another purpose of this book is to show that the generalized net theory can be successfully used as a new description of multilayer neural networks. Several generalized net descriptions of neural networks functioning processes are considered, namely: the simulation process of networks, a system of neural networks and the learning algorithms developed in this book.
The generalized net approach to modelling of real systems may be used successfully for the description of a variety of technological and intellectual problems, it can be used not only for representing the parallel functioning of homogenous objects, but also for modelling non-homogenous systems, for example systems which consist of a different kind of subsystems.
The use of the generalized nets methodology shows a new way to describe functioning of discrete dynamic systems.
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Specificații
ISBN-13: 9783319002477
ISBN-10: 3319002473
Pagini: 196
Ilustrații: XII, 182 p.
Dimensiuni: 155 x 235 x 16 mm
Greutate: 0.45 kg
Ediția:2013
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Computational Intelligence
Locul publicării:Cham, Switzerland
ISBN-10: 3319002473
Pagini: 196
Ilustrații: XII, 182 p.
Dimensiuni: 155 x 235 x 16 mm
Greutate: 0.45 kg
Ediția:2013
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Computational Intelligence
Locul publicării:Cham, Switzerland
Public țintă
ResearchCuprins
Introduction to Multilayer Neural Networks.- Basics of Generalized Nets.- Simulation Process of Neural Networks.- Learning from Examples.- Learning as a Control Process.- Parameterisation of Learning.- Adjoint Neural Networks.
Recenzii
From the reviews:
“This book aims to provide a suitable framework allowing the embedding of the multilayer neural networks viewed as multistage systems, in an extension of Petri net theory called the theory of generalized nets. … The developments presented in the book are both interesting and important, and open new perspectives for research in the area … . book is of real value to researchers in the field of neural networks. It is also useful for students studying computer science and engineering.” (L. State, Computing Reviews, April, 2014)
“This book aims to provide a suitable framework allowing the embedding of the multilayer neural networks viewed as multistage systems, in an extension of Petri net theory called the theory of generalized nets. … The developments presented in the book are both interesting and important, and open new perspectives for research in the area … . book is of real value to researchers in the field of neural networks. It is also useful for students studying computer science and engineering.” (L. State, Computing Reviews, April, 2014)
Textul de pe ultima copertă
The primary purpose of this book is to show that a multilayer neural network can be considered as a multistage system, and then that the learning of this class of neural networks can be treated as a special sort of the optimal control problem. In this way, the optimal control problem methodology, like dynamic programming, with modifications, can yield a new class of learning algorithms for multilayer neural networks.
Another purpose of this book is to show that the generalized net theory can be successfully used as a new description of multilayer neural networks. Several generalized net descriptions of neural networks functioning processes are considered, namely: the simulation process of networks, a system of neural networks and the learning algorithms developed in this book.
The generalized net approach to modelling of real systems may be used successfully for the description of a variety of technological and intellectual problems, it can be used not only for representing the parallel functioning of homogenous objects, but also for modelling non-homogenous systems, for example systems which consist of a different kind of subsystems.
The use of the generalized nets methodology shows a new way to describe functioning of discrete dynamic systems.
Another purpose of this book is to show that the generalized net theory can be successfully used as a new description of multilayer neural networks. Several generalized net descriptions of neural networks functioning processes are considered, namely: the simulation process of networks, a system of neural networks and the learning algorithms developed in this book.
The generalized net approach to modelling of real systems may be used successfully for the description of a variety of technological and intellectual problems, it can be used not only for representing the parallel functioning of homogenous objects, but also for modelling non-homogenous systems, for example systems which consist of a different kind of subsystems.
The use of the generalized nets methodology shows a new way to describe functioning of discrete dynamic systems.
Caracteristici
Recent research on Multilayer Neural Networks Shows that a multilayer neural network can be considered as a multistage system, and that the learning of this class of neural networks can be treated as a special sort of the optimal control problem Presents a new way to describe the functioning of discrete dynamic systems Shows that the generalized net theory developed by Atanassov (1984) as the extension of the ordinary Petri net theory and its modifications can be successfully used as a new description of multilayer neural networks