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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
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.
Content Level »Research
Keywords »Computational Intelligence - Discrete Dynamic Systems - Multilayer Neural Networks - Neural Networks - Optimal Control
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.