Cantitate/Preț
Produs

Multilayer Neural Networks: A Generalized Net Perspective: Studies in Computational Intelligence, cartea 478

Autor Maciej Krawczak
en Limba Engleză Hardback – 31 mai 2013
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.
 
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 64051 lei  6-8 săpt.
  Springer International Publishing – 23 iun 2015 64051 lei  6-8 săpt.
Hardback (1) 64680 lei  6-8 săpt.
  Springer International Publishing – 31 mai 2013 64680 lei  6-8 săpt.

Din seria Studies in Computational Intelligence

Preț: 64680 lei

Preț vechi: 80850 lei
-20% Nou

Puncte Express: 970

Preț estimativ în valută:
12378 12844$ 10346£

Carte tipărită la comandă

Livrare economică 15-29 martie

Preluare comenzi: 021 569.72.76

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

Public țintă

Research

Cuprins

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)

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.
 

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