Earticle

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

키워드

저자

• Ming Yang [ Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China ]
• Wei-feng SHI [ Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605