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008			170427s2017 sz a ob 000 0 eng d
020			_a331957812X
020			_a3319578138 _q(electronic bk.)
020			_a9783319578125
020			_a9783319578132 _q(electronic bk.)
020			_z9783319578125 _q(print)
040			_aN$T _cN$T _dGW5XE _dEBLCP _dN$T _dUAB _dMERER _dYDX _dESU _dOCLCF _dCOO _dOCLCQ _dNJR _dOTZ _dAZU _dUPM _dVT2 _dIOG _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aQA76.9.S63 _bD539 2017 EB
100	1		_aDíaz Cortés, Margarita Arimatea _eautor _998432
245	1	0	_aEngineering applications of soft computing _cMargarita-Arimatea Díaz-Cortés, Erik Cuevas, Raúl Rojas.
264		1	_aCham, Switzerland _bSpringer _c2017.
300			_a1 recurso en línea (xv, 258 páginas) _bilustraciones
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aIntelligent systems reference library _x1868-4394 _vvolume 129
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas
505	0		_aPreface; Contents; 1 Introduction; 1.1 Soft Computing; 1.2 Fuzzy Logic; 1.3 Neural Networks; 1.4 Evolutionary Computation; 1.5 Definition of an Optimization Problem; 1.6 Classical Optimization; 1.7 Optimization with Evolutionary Computation; 1.8 Soft Computing in Engineering; References; 2 Motion Estimation Algorithm Using Block-Matching and Harmony Search Optimization; 2.1 Introduction; 2.2 Harmony Search Algorithm; 2.2.1 The Harmony Search Algorithm; 2.2.1.1 Initializing the Problem and Algorithm Parameters; 2.2.1.2 Harmony Memory Initialization; 2.2.1.3 Improvisation of New Harmony Vectors.
505	8		_a2.2.1.4 Updating the Harmony Memory2.2.2 Computational Procedure; 2.3 Fitness Approximation Method; 2.3.1 Updating the Individual Database; 2.3.2 Fitness Calculation Strategy; 2.3.3 HS Optimization Method; 2.4 Motion Estimation and Block-Matching; 2.5 Block-Matching Algorithm Based on Harmony Search with the Estimation Strategy; 2.5.1 Initial Population; 2.5.2 Tuning of the HS Algorithm; 2.5.3 The HS-BM Algorithm; 2.5.4 Discussion on the Accuracy of the Fitness Approximation Strategy; 2.6 Experimental Results; 2.6.1 HS-BM Results; 2.6.2 Results on H.264; 2.7 Conclusions; References.
505	8		_a3 Gravitational Search Algorithm Applied to Parameter Identification for Induction Motors3.1 Introduction; 3.2 Problem Statement; 3.3 Gravitational Search Algorithm; 3.4 Experimental Results; 3.4.1 Induction Motor Parameter Identification; 3.4.2 Statistical Analysis; 3.5 Conclusions; References; 4 Color Segmentation Using LVQ Neural Networks; 4.1 Introduction; 4.1.1 Histogram Thresholding and Color Space Clustering; 4.1.2 Edge Detection; 4.1.3 Probabilistic Methods; 4.1.4 Soft-Computing Techniques; 4.1.5 Scheme; 4.2 Background Issues; 4.2.1 RGB Space Color; 4.2.2 Artificial Neural Networks.
505	8		_a4.3 Competitive Networks4.4 Learning Vectors Quantization Vectors; 4.5 Architecture of the Color Segmentation System; 4.6 Implementation; 4.7 Results and Discussion; 4.8 Conclusions; References; 5 Global Optimization Using Opposition-Based Electromagnetism-Like Algorithm; 5.1 Introduction; 5.2 Electromagnetism: Like Optimization Algorithm (EMO); 5.2.1 Initialization; 5.2.2 Local Search; 5.2.3 Total Force Vector Computation; 5.2.4 Movement; 5.3 Opposition-Based Learning (OBL); 5.3.1 Opposite Number; 5.3.2 Opposite Point; 5.3.3 Opposite-Based Optimization.
505	8		_a5.4 Opposition-Based Electromagnetism-Like Optimization Algorithm5.4.1 Opposition-Based Population Initialization; 5.4.2 Opposition-Based Production for New Generation; 5.5 Experimental Results; 5.5.1 Test Problems; 5.5.2 Parameter Settings for the Involved EMO Algorithms; 5.5.3 Results; 5.6 Conclusions; References; 6 Multi-threshold Segmentation Using Learning Automata; 6.1 Introduction; 6.2 Gaussian Approximation; 6.3 Learning Automata (LA); 6.3.1 CARLA Algorithm; 6.4 Implementation; 6.5 Experimental Results; 6.5.1 LA Algorithm Performance in Image Segmentation.
520	3		_aThis book bridges the gap between Soft Computing techniques and their applications to complex engineering problems. In each chapter we endeavor to explain the basic ideas behind the proposed applications in an accessible format for readers who may not possess a background in some of the fields. Therefore, engineers or practitioners who are not familiar with Soft Computing methods will appreciate that the techniques discussed go beyond simple theoretical tools, since they have been adapted to solve significant problems that commonly arise in such areas. At the same time, the book will show members of the Soft Computing community how engineering problems are now being solved and handled with the help of intelligent approaches. Highlighting new applications and implementations of Soft Computing approaches in various engineering contexts, the book is divided into 12 chapters. Further, it has been structured so that each chapter can be read independently of the others.
650		7	_aSoft computing. _2fast _0(OCoLC)fst01124115 _9166276 _0comprobar BNE20061117628
700	1		_aCuevas, Erik, _eautor _944351
700	1		_aRojas, Raúl, _d1955- _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-57813-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017C
998			_b02/2018 _dz _e- _zSI
999			_c95885 _d95885 _x1