000			03689nam a22004815i 4500
001			978-3-642-29491-4
003			DE-He213
005			20200421111845.0
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. _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		0	_aComputational intelligence.
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
942			_cEBK
999			_c55794 _d55794