000 03655nam a22004575i 4500
001 978-3-642-29491-4
003 DE-He213
005 20140220082847.0
007 cr nn 008mamaa
008 120727s2013 gw | s |||| 0|eng d
020 _a9783642294914
_9978-3-642-29491-4
024 7 _a10.1007/978-3-642-29491-4
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aSuresh, Sundaram.
_eauthor.
245 1 0 _aSupervised Learning with Complex-valued Neural Networks
_h[electronic resource] /
_cby Sundaram Suresh, Narasimhan Sundararajan, Ramasamy Savitha.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aXXII, 170 p. 37 illus., 6 illus. in color.
_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 ;
_v421
505 0 _aIntroduction -- Fully Complex-valued Multi Layer Perceptron Networks -- Fully Complex-valued Radial Basis Function Networks -- Performance Study on Complex-valued Function Approximation Problems -- Circular Complex-valued Extreme Learning Machine Classifier -- Performance Study on Real-valued Classification Problems -- Complex-valued Self-regulatory Resource Allocation Network -- Conclusions and Scope for FutureWorks (CSRAN).
520 _aRecent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks.  Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computation time of the training process is critical, a fast learning complex-valued neural network called as a fully complex-valued relaxation network along with its learning algorithm has been presented. The presence of orthogonal decision boundaries helps complex-valued neural networks to outperform real-valued networks in performing classification tasks. This aspect has been highlighted. The performances of various complex-valued neural networks are evaluated on a set of benchmark and real-world function approximation and real-valued classification problems.
650 0 _aEngineering.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aSignal, Image and Speech Processing.
700 1 _aSundararajan, Narasimhan.
_eauthor.
700 1 _aSavitha, Ramasamy.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642294907
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v421
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-29491-4
912 _aZDB-2-ENG
999 _c96909
_d96909