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
Link To Document