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008			170407s2017 sz a ob 000 0 eng d
020			_a3319559893 _q(electronic bk.)
020			_a9783319559896 _q(electronic bk.)
020			_z3319559877
020			_z9783319559872 _q(print)
040			_aN$T _cN$T _dEBLCP _dGW5XE _dYDX _dN$T _dOCLCF _dUAB _dESU _dAZU _dUPM _dCOO _dOTZ _dVT2 _dOCLCQ _dIOG _dU3W _dES-MaUEC _bspa
050		4	_aQA76.9.N37 _bZ436 2017 EB
100	1		_aZhang, Gexiang _eautor _9101997
245	1	0	_aReal-life applications with membrane computing _cGexiang Zhang, Mario J. Pérez-Jiménez, Marian Gheorghe.
264		1	_aCham, Switzerland _bSpringer _c2017.
300			_a1 recurso en línea (xii, 355 páginas) _bilustraciones (algunas a color)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aEmergence, complexity and computation _x2194-7287 _vvolume 25
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas
505	0		_aPreface; References; Contents; 1 Membrane Computing -- Key Concepts and Definitions; 1.1 Introduction; 1.2 Origins of Membrane Computing; 1.3 Preliminary Concepts and Notations; 1.4 Membrane Computing Concepts; 1.5 Summary; References; 2 Fundamentals of Evolutionary Computation; 2.1 Introduction; 2.2 Genetic Algorithms; 2.3 Quantum-Inspired Evolutionary Algorithms; 2.4 Ant Colony Optimization; 2.5 Particle Swarm Optimization; 2.6 Differential Evolution; 2.7 Conclusions; References; 3 Membrane Algorithms; 3.1 Introduction; 3.2 Membrane Algorithms with Nested Membrane Structure; 3.2.1 Principle.
505	8		_a3.2.2 Genetic Algorithm Based on P System3.3 Membrane Algorithms with One-Level Membrane Structure; 3.3.1 Principle; 3.3.2 Quantum-Inspired Evolutionary Algorithm Based on P Systems; 3.3.3 Ant Colony Optimization Based on P Systems; 3.3.4 Differential Evolution Based on P Systems; 3.4 Membrane Algorithms with Hybrid Hierarchical Membrane Structure; 3.5 Membrane Algorithms with Dynamic Hierarchical Membrane Structure; 3.5.1 Brief Introduction; 3.5.2 Approximate Algorithm Using P Systems with Active Membranes; 3.6 Membrane Algorithms with Static Network Structure; 3.6.1 Brief Introduction.
505	8		_a3.6.2 A Hybrid Approach Based on Differential Evolution and Tissue P Systems3.7 Membrane Algorithms with Dynamic Network Structure; 3.7.1 Brief Introduction; 3.7.2 Population Membrane-System-Inspired Evolutionary Algorithm; 3.7.3 Multi-objective Membrane Algorithm Based on Population P Systems and DE; 3.8 P Systems Roles in Membrane Algorithms; 3.8.1 Population Diversity Analysis; 3.8.2 Convergence Analysis; 3.9 Conclusions; References; 4 Engineering Optimization with Membrane Algorithms; 4.1 Introduction; 4.2 Engineering Optimizations with Cell-Like P Systems; 4.2.1 Signal Analysis.
505	8		_a4.2.2 Image Processing4.2.3 Controller Design; 4.2.4 Mobile Robot Path Planning; 4.2.5 Other Applications; 4.3 Engineering Optimization with Tissue-Like P Systems; 4.3.1 Manufacturing Parameter Optimization Problems; 4.3.2 Distribution Network Reconfiguration; 4.4 Engineering Optimization with Neural-Like P Systems; 4.5 Conclusions; References; 5 Electric Power System Fault Diagnosis with Membrane Systems; 5.1 Introduction; 5.2 Preliminaries; 5.2.1 Fuzzy Knowledge Representation and Reasoning; 5.2.2 Essentials of Electric Power System Fault Diagnosis.
505	8		_a5.2.3 Principles of Model-Based Fault Diagnosis Methods5.3 Spiking Neural P Systems for Fault Diagnosis; 5.3.1 Models; 5.3.2 Algorithms; 5.4 Fault Diagnosis with Spiking Neural P Systems; 5.4.1 Transformer Fault Diagnosis with rFRSN P Systems; 5.4.2 Traction Power Supply Systems Fault Diagnosis with WFRSN P Systems; 5.4.3 Power Transmission Networks Fault Diagnosis with tFRSN P Systems; 5.5 Conclusions; References; 6 Robot Control with Membrane Systems; 6.1 Introduction; 6.2 Numerical P Systems; 6.2.1 NPS; 6.2.2 An Example for NPS; 6.2.3 ENPS; 6.3 Preliminaries of Mobile Robot Control.
520	3		_aThis book thoroughly investigates the underlying theoretical basis of membrane computing models, and reveals their latest applications. In addition, to date there have been no illustrative case studies or complex real-life applications that capitalize on the full potential of the sophisticated membrane systems computational apparatus; gaps that this book remedies. By studying various complex applications - including engineering optimization, power systems fault diagnosis, mobile robot controller design, and complex biological systems involving data modeling and process interactions - the book also extends the capabilities of membrane systems models with features such as formal verification techniques, evolutionary approaches, and fuzzy reasoning methods. As such, the book offers a comprehensive and up-to-date guide for all researchers, PhDs and undergraduate students in the fields of computer science, engineering and the bio-sciences who are interested in the applications of natural computing models.
650		7	_aComputación evolutiva _2embne _0(OCoLC)fst00917338 _0 _9667195
700	1		_aGheorghe, Marian, _d1953- _eautor
700	1		_aPérez-Jiménez, Mario J., _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-55989-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017C
998			_b02/2018 _dz _e- _zSI
999			_c95770 _d95770 _x1