Feature generation in fault diagnosis based on immune programming

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@InProceedings{Li:2009:CIRA,

• author = "Maolin Li and Lin Liang and Sunan Wang and Xiaohu Li",
• title = "Feature generation in fault diagnosis based on immune programming",
• booktitle = "2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA)",
• year = "2009",
• month = "15-18 " # dec,
• pages = "183--187",
• abstract = "In the symptom feature discovery, genetic programming has the shortage of premature convergence. So a new feature generation method based on immune programming is put forward. The new features are constructed by polynomial expressions of the original features. And then, with the immune operators such as antibody representation and mutation of tree-like structure, affinity function defined by classification performance of every individual, the clonal selection optimal algorithm is adopted to search the best feature that has excellent classification performance. The experiments of sound signal for gasoline engine show that, due to the diversity of antibodies is maintained by clonal selection principle, the best compound feature founded by immune programming has better classification ability than feature optimism by genetic programming.",
• keywords = "genetic algorithms, genetic programming, affinity function, antibody representation, clonal selection optimal algorithm, fault diagnosis, feature generation, immune programming, polynomial expressions, premature convergence, symptom feature discovery, tree-like structure, fault diagnosis, pattern recognition, polynomials",
• DOI = "doi:10.1109/CIRA.2009.5423210",
• notes = "Sch. of Mech. Eng. & the Eng. Workshop, Xi'an Jiaotong Univ., Xi'an, China. Also known as \cite{5423210}",

}

Genetic Programming entries for Maolin Li Lin Liang Sunan Wang Xiaohu Li

Citations