• DocumentCode
    2516502
  • Title

    The application of adaptive genetic reduction algorithm in radar faults diagnosis

  • Author

    Wei, Pan ; Jiahe, Xu ; Lei, Li

  • Author_Institution
    Electr. Detection Dept., Shenyang Artillery Acad., Shenyang, China
  • fYear
    2011
  • fDate
    23-25 May 2011
  • Firstpage
    1703
  • Lastpage
    1707
  • Abstract
    An improved adaptive genetic reduction algorithm based on knowledge dependability is presented in this paper when rough set theory and genetic algorithm are researched. There are four characters of the algorithm, which the initial population of binary code is restricted by attribute core of decision table, the fitness function in the algorithm is define through the dependability that decision attribute for condition attribute, the adaptive crossover probability and adaptive mutation probability are improved, and the new generation individuals are added correction operator. Using the algorithm radar faults is diagnosed and the simple rules can be obtained automatically. The diagnosis rules have the characteristic that fault premonitions and fault reasons parallelism can be correlated one by one. The defects of worse system veracity and lower efficiency can be avoided about expert fault diagnosis based on traditional fault tree.
  • Keywords
    decision tables; expert systems; fault diagnosis; genetic algorithms; radar; rough set theory; telecommunication computing; adaptive crossover probability; adaptive genetic reduction algorithm; adaptive mutation probability; binary code; decision table; expert fault diagnosis; fault premonitions; fault reasons parallelism; fitness function; knowledge dependability; radar faults diagnosis; rough set theory; Algorithm design and analysis; Biological cells; Encoding; Fault diagnosis; Genetic algorithms; Genetics; Radar; adaptive genetic algorithm; knowledge dependability; radar faults diagnosis; reduction; rough set;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Decision Conference (CCDC), 2011 Chinese
  • Conference_Location
    Mianyang
  • Print_ISBN
    978-1-4244-8737-0
  • Type

    conf

  • DOI
    10.1109/CCDC.2011.5968470
  • Filename
    5968470