Multilayer Neural Networks: A Generalized Net Perspective

ISBN-13: 9783319033907 / Angielski / Miękka / 2015 / 182 str.

Maciej Krawczak

Opis

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.

Recenzje

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)

Spis treści

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.