원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.8 No.11
2015.11
pp.413-422
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
In view of fault diagnosis of the ship generator , the paper proposes improved fault diagnosis method of ship generator ,which is Optimized Neural Network based on Multi-population Chaos Genetic Algorithm. The results prove that the method effectively solves low precision,slow constringency and local minimum of neural network and improves global search ability, optimizes the rate and precision of fault diagnosis. The method has a certain application prospect for the ship power system generator fault diagnosis.
목차
Abstract
1. Introduction
2. Normal Faults in Ship Generator and Ways to Detect
2.1. Normal Faults in Ship Generator
2.2. The First Coordinate Transformation for Substructures
3. Multi-population Chaos Genetic Neural Network
3.1. Chaos Optimization Algorithm
3.2. Genetic Algorithm
4. Design of Multi-population Chaos Genetic Neural Network
4.1. Network Creation and Population Initialization Code
4.2. Chaotic Initial Population Generation
4.3. Fitness Calculation
4.4. Selection
4.5. Crossover
4.6. Variation
4.7. Chaos Optimization of Excellent Individuals
4.8. Multi-population Genetic Algorithm
4.9. Fusion
5. Examples of Fault Diagnosis of Ship Generators
References
1. Introduction
2. Normal Faults in Ship Generator and Ways to Detect
2.1. Normal Faults in Ship Generator
2.2. The First Coordinate Transformation for Substructures
3. Multi-population Chaos Genetic Neural Network
3.1. Chaos Optimization Algorithm
3.2. Genetic Algorithm
4. Design of Multi-population Chaos Genetic Neural Network
4.1. Network Creation and Population Initialization Code
4.2. Chaotic Initial Population Generation
4.3. Fitness Calculation
4.4. Selection
4.5. Crossover
4.6. Variation
4.7. Chaos Optimization of Excellent Individuals
4.8. Multi-population Genetic Algorithm
4.9. Fusion
5. Examples of Fault Diagnosis of Ship Generators
References
저자정보
참고문헌
자료제공 : 네이버학술정보