TY  - BOOK
AU  - Levitin,Gregory
ED  - SpringerLink (Online service)
TI  - Computational Intelligence in Reliability Engineering: New Metaheuristics, Neural and Fuzzy Techniques in Reliability 
T2  - Studies in Computational Intelligence,
SN  - 9783540373728
AV  - TA329-348 
U1  - 519 23
PY  - 2007///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Engineering
KW  - Artificial intelligence
KW  - Mathematics
KW  - Engineering mathematics
KW  - System safety
KW  - Appl.Mathematics/Computational Methods of Engineering
KW  - Artificial Intelligence (incl. Robotics)
KW  - Quality Control, Reliability, Safety and Risk
KW  - Applications of Mathematics
KW  - Computational Intelligence
N1  - The Ant Colony Paradigm for Reliable Systems Design -- Modified Great Deluge Algorithm versus Other Metaheuristics in Reliability Optimization -- Applications of the Cross-Entropy Method in Reliability -- Particle Swarm Optimization in Reliability Engineering -- Cellular Automata and Monte Carlo Simulation for Network Reliability and Availability Assessment -- Network Reliability Assessment through Empirical Models using a Machine Learning Approach -- Neural Networks for Reliability-Based Optimal Design -- Software Reliability Predictions using Artificial Neural Networks -- Computation Intelligence in Online Reliability Monitoring -- Imprecise Reliability: An Introductory Overview -- Posbist Reliability Theory for Coherent Systems -- Analyzing Fuzzy System Reliability Based on the Vague Set Theory -- Fuzzy Sets in the Evaluation of Reliability -- Grey Differential Equation GM(1,1) Models in Repairable System Modeling
N2  - This volume contains chapters presenting applications of different metaheuristics (ant colony optimization, great deluge algorithm, cross-entropy method and particle swarm optimization) in reliability engineering. It also includes chapters devoted to cellular automata and support vector machines and different applications of artificial neural networks, a powerful adaptive technique that can be used for learning, prediction and optimization. Several chapters describe different aspects of imprecise reliability and applications of fuzzy and vague set theory
UR  - http://dx.doi.org/10.1007/978-3-540-37372-8
ER  -