An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors
المؤلفون المشاركون
Li, Kun
Li, Kun
Tian, Huixin
Shuai, Minwei
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-05-02
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
With the continuous improvement of automation in industrial production, industrial process data tends to arrive continuously in many cases.
The ability to handle large amounts of data incrementally and efficiently is indispensable for modern machine learning (ML) algorithms.
According to the characteristics of industrial production process, we address an ILES (incremental learning ensemble strategy) that incorporates incremental learning to extract information efficiently from constantly incoming data.
The ILES aggregates multiple sublearning machines by different weights for better accuracy.
When new data set arrives, a new submachine will be trained and aggregated into ensemble soft sensor model according to its weight.
The other submachines' weights will be updated at the same time.
Then a new updated soft sensor ensemble model can be obtained.
The weight updating rules are designed by considering the prediction accuracy of submachines with new arrived data.
So the update can fit the data change and obtain new information efficiently.
The sizing percentage soft sensor model is established to learn the information from the production data in the sizing of industrial processes and to test the performance of ILES, where the ELM (Extreme Learning Machine) is selected as the sublearning machine.
The comparison is done among new method, single ELM, AdaBoost.R ELM, and OS-ELM, and the test of the extensions is done with three test functions.
The results of the experiments demonstrate that the soft sensor model based on the ILES has the best accuracy and ability of online updating.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Tian, Huixin& Shuai, Minwei& Li, Kun& Li, Kun. 2019. An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors. Complexity،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1132086
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Tian, Huixin…[et al.]. An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors. Complexity No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1132086
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Tian, Huixin& Shuai, Minwei& Li, Kun& Li, Kun. An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors. Complexity. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1132086
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1132086
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر