000			03035nam a22003735c 4500
006			a||||fo|||| 00| 0
007			cr nn nnnaamaa
650		7	_aRedes neuronales artificiales _2embne _9678664
710	2		_aSpringerLink (Online service) _9106996
999			_c111667 _d111667 _x1
001			111667
003			ES-MaUEC
005			20230102113522.0
008			180626s2019 gw a o |||| 0|eng d
020			_a9783319930251
024	7		_a10.1007/978-3-319-93025-1 _2doi
040			_bspa _aES-MaUEC _cES-MaUEC
050		4	_aQA76.87 _b2019 EB
100	1		_aMirjalili, Seyedali _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9671160
245	1	0	_aEvolutionary Algorithms and Neural Networks : _bTheory and Applications _cby Seyedali Mirjalili.
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2019.
300			_a1 recurso en línea (XIV, 156 páginas) _b68 ilustraciones,60 ilustraciones a color
347			_atext file _bPDF
490	0		_aStudies in Computational Intelligence _x1860-949X _v780
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aPart I: Evolutionary algorithms -- Introduction to Evolutionary Single-objective Optimisation -- Particle Swarm Optimisation -- Ant Colony Optimization -- Genetic Algorithm -- Biogeography-Based Optimization -- Part II: Evolutionary Neural Networks -- Evolutionary Feedforward Neural Networks -- Evolutionary Multi-Layer Perceptron -- Evolutionary Radial Basis Function Networks -- Evolutionary Deep Neural Networks.
520	3		_aThis book introduces readers to the fundamentals of artificial neural networks, with a special emphasis on evolutionary algorithms. At first, the book offers a literature review of several well-regarded evolutionary algorithms, including particle swarm and ant colony optimization, genetic algorithms and biogeography-based optimization. It then proposes evolutionary version of several types of neural networks such as feed forward neural networks, radial basis function networks, as well as recurrent neural networks and multi-later perceptron. Most of the challenges that have to be addressed when training artificial neural networks using evolutionary algorithms are discussed in detail. The book also demonstrates the application of the proposed algorithms for several purposes such as classification, clustering, approximation, and prediction problems. It provides a tutorial on how to design, adapt, and evaluate artificial neural networks as well, and includes source codes for most of the proposed techniques as supplementary materials.
776	0	8	_iPrinted edition: _z9783319930244
776	0	8	_iPrinted edition: _z9783319930268
776	0	8	_iPrinted edition: _z9783030065720
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-93025-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aPrimersemestre_2019_Robotics
998			_aSI _a_alco _a_vill _b10/2019 _cm _dz _ea _feng _ggw _h0