Real-life applications with membrane computing

Abstract

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. 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.