Real-life applications with membrane computing

This 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 membr...

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Bibliographic Details
Main Authors: Zhang, Gexiang, (Author), Pérez-Jiménez, Mario J., (Author), Gheorghe, Marian, 1953- (Author)
Format: eBook
Language: English
Published: Cham, Switzerland : Springer, 2017.
Series: Emergence, complexity and computation ; 25.
Subjects:
Natural computation.
Membranes (Technology)
Evolutionary computation.
elektronické knihy
electronic books
ISBN: 9783319559896
9783319559872
Physical Description: 1 online resource (xii, 355 pages) : illustrations (some color)

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100 1 |a Zhang, Gexiang,  |e author. 
245 1 0 |a Real-life applications with membrane computing /  |c Gexiang Zhang, Mario J. Pérez-Jiménez, Marian Gheorghe. 
264 1 |a Cham, Switzerland :  |b Springer,  |c 2017. 
300 |a 1 online resource (xii, 355 pages) :  |b illustrations (some color) 
336 |a text  |b txt  |2 rdacontent 
337 |a počítač  |b c  |2 rdamedia 
338 |a online zdroj  |b cr  |2 rdacarrier 
490 1 |a Emergence, complexity and computation,  |x 2194-7287 ;  |v volume 25 
505 0 |a Preface; 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 |a 3.2.2 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 |a 3.6.2 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 |a 4.2.2 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 |a 5.2.3 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. 
504 |a Includes bibliographical references. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a This 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. 
590 |a SpringerLink  |b Springer Complete eBooks 
650 0 |a Natural computation. 
650 0 |a Membranes (Technology) 
650 0 |a Evolutionary computation. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Pérez-Jiménez, Mario J.,  |e author. 
700 1 |a Gheorghe, Marian,  |d 1953-  |e author. 
776 0 8 |i Print version:  |a Zhang, Gexiang.  |t Real-life applications with membrane computing.  |d Cham, Switzerland : Springer, 2017  |z 9783319559872  |z 3319559877  |w (OCoLC)973919739 
830 0 |a Emergence, complexity and computation ;  |v 25. 
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999 |c 99984  |d 99984 
993 |x NEPOSILAT  |y EIZ 

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