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...

Full description

Saved in:
Bibliographic Details
Main Authors Zhang, Gexiang (Author), Pérez-Jiménez, Mario J. (Author), Gheorghe, Marian, 1953- (Author)
Format Electronic eBook
LanguageEnglish
Published Cham, Switzerland : Springer, 2017.
SeriesEmergence, complexity and computation ; 25.
Subjects
Natural computation.
Membranes (Technology)
Evolutionary computation.
elektronické knihy
electronic books
Online AccessFull text
ISBN9783319559896
9783319559872
ISSN2194-7287 ;
Physical Description1 online resource (xii, 355 pages) : illustrations (some color)

Cover

Table of Contents:
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

Similar Items