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Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network
Joint Authors
Li, Li-li
Gao, Jian-min
Li, Hui
Chen, Kun
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-12
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Aiming at the problems of the lack of abnormal instances and the lag of quality anomaly discovery in quality database, this paper proposed the method of recognizing quality anomaly from the quality control chart data by probabilistic neural network (PNN) optimized by improved genetic algorithm, which made up deficiencies of SPC control charts in practical application.
Principal component analysis (PCA) reduced the dimension and extracted the feature of the original data of a control chart, which reduced the training time of PNN.
PNN recognized successfully both single pattern and mixed pattern of control charts because of its simple network structure and excellent recognition effect.
In order to eliminate the defect of experience value, the key parameter of PNN was optimized by the improved (SGA) single-target optimization genetic algorithm, which made PNN achieve a higher rate of recognition accuracy than PNN optimized by standard genetic algorithm.
Finally, the above method was validated by a simulation experiment and proved to be the most effective method compared with traditional BP neural network, single PNN, PCA-PNN without parameters optimized, and SVM optimized by particle swarm optimization algorithm.
American Psychological Association (APA)
Li, Li-li& Chen, Kun& Gao, Jian-min& Li, Hui. 2020. Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network. Shock and Vibration،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1210221
Modern Language Association (MLA)
Li, Li-li…[et al.]. Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network. Shock and Vibration No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1210221
American Medical Association (AMA)
Li, Li-li& Chen, Kun& Gao, Jian-min& Li, Hui. Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network. Shock and Vibration. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1210221
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1210221