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A Hybrid Ensemble Model Based on ELM and Improved AdaBoost.RT Algorithm for Predicting the Iron Ore Sintering Characters
المؤلفون المشاركون
Zou, Zong-Shu
Wang, Sen-Hui
Li, Hai-Feng
Zhang, Yong-Jie
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-01-17
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
As energy efficiency becomes increasingly important to the steel industry, the iron ore sintering process is attracting more attention since it consumes the second large amount of energy in the iron and steel making processes.
The present work aims to propose a prediction model for the iron ore sintering characters.
A hybrid ensemble model combined the extreme learning machine (ELM) with an improved AdaBoost.RT algorithm is developed for regression problem.
First, the factors that affect solid fuel consumption, gas fuel consumption, burn-through point (BTP), and tumbler index (TI) are ranked according to the attributes weightiness sequence by applying the RReliefF method.
Second, the ELM network is selected as an ensemble predictor due to its fast learning speed and good generalization performance.
Third, an improved AdaBoost.RT is established to overcome the limitation of conventional AdaBoost.RT by dynamically self-adjusting the threshold value.
Then, an ensemble ELM is employed by using the improved AdaBoost.RT for better precision than individual predictor.
Finally, this hybrid ensemble model is applied to predict the iron ore sintering characters by production data from No.
4 sintering machine in Baosteel.
The results obtained show that the proposed model is effective and feasible for the practical sintering process.
In addition, through analyzing the first superior factors, the energy efficiency and sinter quality could be obviously improved.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Sen-Hui& Li, Hai-Feng& Zhang, Yong-Jie& Zou, Zong-Shu. 2019. A Hybrid Ensemble Model Based on ELM and Improved AdaBoost.RT Algorithm for Predicting the Iron Ore Sintering Characters. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1129437
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Sen-Hui…[et al.]. A Hybrid Ensemble Model Based on ELM and Improved AdaBoost.RT Algorithm for Predicting the Iron Ore Sintering Characters. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1129437
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Sen-Hui& Li, Hai-Feng& Zhang, Yong-Jie& Zou, Zong-Shu. A Hybrid Ensemble Model Based on ELM and Improved AdaBoost.RT Algorithm for Predicting the Iron Ore Sintering Characters. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1129437
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1129437
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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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
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