000			06280nam a2200889 i 4500
001			5732789
003			IEEE
005			20220712205756.0
006			m o d
007			cr |n|||||||||
008			151221s2011 njua ob 001 eng d
020			_a9780470638286 _qebook
020			_z0470638281 _qelectronic
024	7		_a10.1002/9780470638286 _2doi
035			_a(CaBNVSL)mat05732789
035			_a(IDAMS)0b000064814ebff9
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQA76.87 _b.C525 2010eb
100	1		_aCirrincione, Giansalvo, _d1959- _927630
245	1	0	_aNeural-based orthogonal data fitting : _bthe EXIN neural networks / _cGiansalvo Cirrincione, Maurizio Cirrincione.
264		1	_aHoboken, New Jersey : _bWiley, _cc2010.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2011]
300			_a1 PDF (xviii, 243 pages, [12] pages) : _billustrations (some color).
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aAdaptive and learning systems for signal processing, communications and control series ; _v38
504			_aIncludes bibliographical references and index.
505	0		_aForeword -- Preface -- 1 The Total Least Squares Problems -- 1.1 Introduction -- 1.2 Some TLS Applications -- 1.3 Preliminaries -- 1.4 Ordinary Least Squares Problems -- 1.5 Basic TLS Problem -- 1.6 Multidimensional TLS Problem -- 1.7 Nongeneric Unidimensional TLS Problem -- 1.8 Mixed OLS-TLS Problem -- 1.9 Algebraic Comparisons Between TLS and OLS -- 1.10 Statistical Properties and Validity -- 1.11 Basic Data Least Squares Problem -- 1.12 The Partial TLS Algorithm -- 1.13 Iterative Computation Methods -- 1.14 Rayleigh Quotient Minimization Non Neural and Neural Methods -- 2 The MCA EXIN Neuron -- 2.1 The Rayleigh Quotient -- 2.2 The Minor Component Analysis -- 2.3 The MCA EXIN Linear Neuron -- 2.4 The Rayleigh Quotient Gradient Flows -- 2.5 The MCA EXIN ODE Stability Analysis -- 2.6 Dynamics of the MCA Neurons -- 2.7 Fluctuations (Dynamic Stability) and Learning Rate -- 2.8 Numerical Considerations -- 2.9 TLS Hyperplane Fitting -- 2.10 Simulations for the MCA EXIN Neuron -- 2.11 Conclusions -- 3 Variants of the MCA EXIN Neuron -- 3.1 High-Order MCA Neurons -- 3.2 The Robust MCA EXIN Nonlinear Neuron (NMCA EXIN) -- 3.3 Extensions of the Neural MCA -- 4 Introduction to the TLS EXIN Neuron -- 4.1 From MCA EXIN to TLS EXIN -- 4.2 Deterministic Proof and Batch Mode -- 4.3 Acceleration Techniques -- 4.4 Comparison with TLS GAO -- 4.5 A TLS Application: Adaptive IIR Filtering -- 4.6 Numerical Considerations -- 4.7 The TLS Cost Landscape: Geometric Approach -- 4.8 First Considerations on the TLS Stability Analysis -- 5 Generalization of Linear Regression Problems -- 5.1 Introduction -- 5.2 The Generalized Total Least Squares (GeTLS EXIN) Approach -- 5.3 The GeTLS Stability Analysis -- 5.4 Neural Nongeneric Unidimensional TLS -- 5.5 Scheduling -- 5.6 The Accelerated MCA EXIN Neuron (MCA EXIN+) -- 5.7 Further Considerations -- 5.8 Simulations for the GeTLS EXIN Neuron -- 6 The GeMCA EXIN Theory -- 6.1 The GeMCA Approach -- 6.2 Analysis of Matrix K -- 6.3 Analysis of the Derivative of the Eigensystem of GeTLS EXIN.
505	8		_a6.4 Rank One Analysis Around the TLS Solution -- 6.5 The GeMCA Spectra -- 6.6 Qualitative Analysis of the Critical Points of the GeMCA EXIN Error Function -- 6.7 Conclusion -- References -- Index.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_a"Written by three leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Where most books on the subject are dedicated to PCA (principal component analysis) and consider MCA (minor component analysis) as simply a consequence, this is the fist book to start from the MCA problem and arrive at important conclusions about the PCA problem."-- _cProvided by publisher.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/21/2015.
650		0	_aNeural networks (Computer science) _93414
650		0	_aNumerical analysis. _94603
650		0	_aOrthogonalization methods. _927631
655		0	_aElectronic books. _93294
695			_aAcceleration
695			_aAccuracy
695			_aAdaptive systems
695			_aApproximation methods
695			_aArtificial intelligence
695			_aBibliographies
695			_aBiological neural networks
695			_aBiomedical measurements
695			_aCorrelation
695			_aCost function
695			_aEigenvalues and eigenfunctions
695			_aEquations
695			_aGeMCA EXIN theory and generalized Rayleigh quotient
695			_aGeMCA spectra
695			_aHebbian theory
695			_aIndexes
695			_aLearning systems
695			_aLinear regression
695			_aLogistics
695			_aMathematical model
695			_aNeurons
695			_aNoise
695			_aOptical distortion
695			_aPrediction algorithms
695			_aPrincipal component analysis
695			_aRobustness
695			_aSignal processing
695			_aSignal processing algorithms
695			_aTraining
695			_aVectors
695			_aanalysis of derivative of eigensystem of GeTLS EXIN
700	1		_aCirrincione, Maurizio, _d1961- _927632
710	2		_aIEEE Xplore (Online Service), _edistributor. _927633
710	2		_aJohn Wiley & Sons, _epublisher. _96902
830		0	_aAdaptive and learning systems for signal processing, communication, and control ; _v38 _927634
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5732789
942			_cEBK
999			_c74124 _d74124