Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network
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, whic...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/6694732 |
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| _version_ | 1849400078369816576 |
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| author | Li-li Li Kun Chen Jian-min Gao Hui Li |
| author_facet | Li-li Li Kun Chen Jian-min Gao Hui Li |
| author_sort | Li-li Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a00774fbf329412e99634584209d1b4e |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-a00774fbf329412e99634584209d1b4e2025-08-20T03:38:11ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/66947326694732Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural NetworkLi-li Li0Kun Chen1Jian-min Gao2Hui Li3State Key Laboratory of Mechanical Manufacturing Systems Engineering, Xi’an Jiao Tong University, Xi’an 710049, ChinaState Key Laboratory of Mechanical Manufacturing Systems Engineering, Xi’an Jiao Tong University, Xi’an 710049, ChinaState Key Laboratory of Mechanical Manufacturing Systems Engineering, Xi’an Jiao Tong University, Xi’an 710049, ChinaState Key Laboratory of Mechanical Manufacturing Systems Engineering, Xi’an Jiao Tong University, Xi’an 710049, ChinaAiming 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.http://dx.doi.org/10.1155/2020/6694732 |
| spellingShingle | Li-li Li Kun Chen Jian-min Gao Hui Li Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network Shock and Vibration |
| title | Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network |
| title_full | Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network |
| title_fullStr | Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network |
| title_full_unstemmed | Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network |
| title_short | Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network |
| title_sort | research on quality anomaly recognition method based on optimized probabilistic neural network |
| url | http://dx.doi.org/10.1155/2020/6694732 |
| work_keys_str_mv | AT lilili researchonqualityanomalyrecognitionmethodbasedonoptimizedprobabilisticneuralnetwork AT kunchen researchonqualityanomalyrecognitionmethodbasedonoptimizedprobabilisticneuralnetwork AT jianmingao researchonqualityanomalyrecognitionmethodbasedonoptimizedprobabilisticneuralnetwork AT huili researchonqualityanomalyrecognitionmethodbasedonoptimizedprobabilisticneuralnetwork |