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Multilayer Neural Networks [electronic resource] : A Generalized Net Perspective / by Maciej Krawczak.

By: Krawczak, Maciej [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 478Publisher: Heidelberg : Springer International Publishing : Imprint: Springer, 2013Description: XII, 182 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319002484.Subject(s): Engineering | Artificial intelligence | Computational intelligence | Control engineering | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics) | ControlAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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.  .
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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.

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.  .

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