000			06395nam a2201177 i 4500
001			5263178
003			IEEE
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006			m o d
007			cr |n|||||||||
008			100317t20152000nyua ob 001 0 eng d
020			_a9780470545362 _qelectronic
020			_z9780780360112 _qprint
020			_z0470545364 _qelectronic
024	7		_a10.1109/9780470545362 _2doi
035			_a(CaBNVSL)mat05263178
035			_a(IDAMS)0b000064810c3334
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
082	0	4	_a610/.285/632
245	0	0	_aNonlinear biomedical signal processing. _nVolume 1, _pFuzzy logic, neural networks, and new algorithms / _cedited by Metin Akay.
246	3		_aFuzzy logic, neural networks, and new algorithms
264		1	_aNew York : _bIEEE Press, _cc2000.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2000]
300			_a1 PDF (276 pages) : _billustrations (some color).
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aIEEE Press series on biomedical engineering ; _v5
500			_a"IEEE Engineering in medicine and biology society, sponsor."
504			_aIncludes bibliographical references and index.
505	0		_aPreface. List of Contributors. Uncertainty Management in Medical Applications (B. Bouchon-Meunier). Applications of Fuzzy Clustering to Biomedical Signal Processing and Dynamic System (A. Geva). Neural Networks: A Guided Tour (S. Haykin). Neural Networks in Processing and Analysis of Biomedical Signals (H. Nazeran & K. Behbehani). Rare Event Detection in Genomic Sequences by Neural Networks and Sample Stratification (W. Choe, et al.). An Axiomatic Approach to Reformulating Radial Basis Neural Networks (N. Karayiannis). Soft Learning Vector Quantization and Clustering Algorithms Based on Reformulation (N. Karayiannis). Metastable Associative Network Models of Neuronal Dynamics Transition During Sleep (M. Nakao & M. Yamamoto). Artificial Neural Networks for Spectroscopic Signal Measurement (C.-W. Lin, et al.). Applications of Feed-Forward Neural Networks in the Electrogastrogram (Z. Lin & J. Chen). Index. About the Editor.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aFor the first time, eleven experts in the fields of signal processing and biomedical engineering have contributed to an edition on the newest theories and applications of fuzzy logic, neural networks, and algorithms in biomedicine. Nonlinear Biomedical Signal Processing, Volume I provides comprehensive coverage of nonlinear signal processing techniques. In the last decade, theoretical developments in the concept of fuzzy logic have led to several new approaches to neural networks. This compilation delivers plenty of real-world examples for a variety of implementations and applications of nonlinear signal processing technologies to biomedical problems. Included here are discussions that combine the various structures of Kohenen, Hopfield, and multiple-layer "designer" networks with other approaches to produce hybrid systems. Comparative analysis is made of methods of genetic, back-propagation, Bayesian, and other learning algorithms. Topics covered include: . Uncertainty management. Analysis of biomedical signals. A guided tour of neural networks. Application of algorithms to EEG and heart rate variability signals. Event detection and sample stratification in genomic sequences. Applications of multivariate analysis methods to measure glucose concentration Nonlinear Biomedical Signal Processing, Volume I is a valuable reference tool for medical researchers, medical faculty and advanced graduate students as well as for practicing biomedical engineers. Nonlinear Biomedical Signal Processing, Volume I is an excellent companion to Nonlinear Biomedical Signal Processing, Volume II: Dynamic Analysis and Modeling.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/21/2015.
650		0	_aSignal processing.
650		0	_aBiomedical engineering.
650		0	_aFuzzy logic.
650		0	_aNeural networks (Computer science)
655		0	_aElectronic books.
695			_aTime series analysis
695			_aTraining
695			_aUncertainty
695			_aVector quantization
695			_aAccuracy
695			_aAdaptive filters
695			_aAlgorithm design and analysis
695			_aArtificial neural networks
695			_aBiographies
695			_aBioinformatics
695			_aBiological neural networks
695			_aBiology
695			_aBiomedical imaging
695			_aBiomedical measurements
695			_aBrain modeling
695			_aBrain models
695			_aCalibration
695			_aClassification algorithms
695			_aClustering algorithms
695			_aComputer architecture
695			_aElectrodes
695			_aFiring
695			_aForecasting
695			_aFunction approximation
695			_aFuzzy set theory
695			_aFuzzy sets
695			_aGenomics
695			_aHippocampus
695			_aHistory
695			_aHumans
695			_aIndexes
695			_aInterpolation
695			_aKnowledge representation
695			_aMachine learning
695			_aMathematical model
695			_aMatrix decomposition
695			_aMinimization
695			_aMonte Carlo methods
695			_aMultilayer perceptrons
695			_aNeurons
695			_aNoise
695			_aPartitioning algorithms
695			_aPattern recognition
695			_aPrediction algorithms
695			_aPrototypes
695			_aRadial basis function networks
695			_aSignal processing algorithms
695			_aSpectral analysis
695			_aStomach
695			_aSugar
695			_aSurface fitting
700	1		_aAkay, Metin.
710	2		_aJohn Wiley & Sons, _epublisher.
710	2		_aIEEE Engineering in Medicine and Biology Society.
710	2		_aIEEE Xplore (Online service), _edistributor.
776	0	8	_iPrint version: _z9780780360112
830		0	_aIEEE Press series on biomedical engineering ; _v5
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5263178
999			_c40089 _d40089