000 03058nam a22004575i 4500
001 978-3-319-00248-4
003 DE-He213
005 20140220082837.0
007 cr nn 008mamaa
008 130418s2013 gw | s |||| 0|eng d
020 _a9783319002484
_9978-3-319-00248-4
024 7 _a10.1007/978-3-319-00248-4
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aKrawczak, Maciej.
_eauthor.
245 1 0 _aMultilayer Neural Networks
_h[electronic resource] :
_bA Generalized Net Perspective /
_cby Maciej Krawczak.
264 1 _aHeidelberg :
_bSpringer International Publishing :
_bImprint: Springer,
_c2013.
300 _aXII, 182 p. 35 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v478
505 0 _aIntroduction 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.
520 _aThe 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.  
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aControl.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319002477
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v478
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-00248-4
912 _aZDB-2-ENG
999 _c96382
_d96382