000			03258nam a22004695i 4500
001			978-3-642-21431-8
003			DE-He213
005			20140220083804.0
007			cr nn 008mamaa
008			110713s2011 gw | s |||| 0|eng d
020			_a9783642214318 _9978-3-642-21431-8
024	7		_a10.1007/978-3-642-21431-8 _2doi
050		4	_aQ342
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
100	1		_aAnastassiou, George A. _eauthor.
245	1	0	_aIntelligent Systems: Approximation by Artificial Neural Networks _h[electronic resource] / _cby George A. Anastassiou.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2011.
300			_aVIII, 108 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aIntelligent Systems Reference Library, _x1868-4394 ; _v19
505	0		_aUnivariate sigmoidal neural network quantitative approximation -- Univariate hyperbolic tangent neural network quantitative approximation -- Multivariate sigmoidal neural network quantitative approximation -- Multivariate hyperbolic tangent neural network quantitative approximation.
520			_aThis brief monograph is the first one to deal exclusively with the quantitative approximation by artificial neural networks to the identity-unit operator. Here we study with rates the approximation properties of the "right" sigmoidal and hyperbolic tangent artificial neural network positive linear operators. In particular we study the degree of approximation of these operators to the unit operator in the univariate and multivariate cases over bounded or unbounded domains. This is given via inequalities and with the use of modulus of continuity of the involved function or its higher order derivative. We examine the real and complex cases.  For the convenience of the reader, the chapters of this book are written in a self-contained style. This treatise relies on author's last two years of related research work. Advanced courses and seminars can be taught out of this brief book. All necessary background and motivations are given per chapter. A related list of references is given also per chapter. The exposed results are expected to find applications in many areas of computer science and applied mathematics, such as neural networks, intelligent systems, complexity theory, learning theory, vision and approximation theory, etc. As such this monograph is suitable for researchers, graduate students, and seminars of the above subjects, also for all science libraries.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650		0	_aMathematics.
650	1	4	_aEngineering.
650	2	4	_aComputational Intelligence.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aApplications of Mathematics.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642214301
830		0	_aIntelligent Systems Reference Library, _x1868-4394 ; _v19
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-21431-8
912			_aZDB-2-ENG
999			_c107978 _d107978