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Adaptive Control Using Fully Online Sequential-Extreme Learning Machine and a Case Study on Engine Air-Fuel Ratio Regulation
المؤلفون المشاركون
Wong, Ka In
Wong, Pak-kin
Vong, Chi Man
Gao, Xiang Hui
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2014، العدد 2014 (31 ديسمبر/كانون الأول 2014)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2014-04-07
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Most adaptive neural control schemes are based on stochastic gradient-descent backpropagation (SGBP), which suffers from local minima problem.
Although the recently proposed regularized online sequential-extreme learning machine (ReOS-ELM) can overcome this issue, it requires a batch of representative initial training data to construct a base model before online learning.
The initial data is usually difficult to collect in adaptive control applications.
Therefore, this paper proposes an improved version of ReOS-ELM, entitled fully online sequential-extreme learning machine (FOS-ELM).
While retaining the advantages of ReOS-ELM, FOS-ELM discards the initial training phase, and hence becomes suitable for adaptive control applications.
To demonstrate its effectiveness, FOS-ELM was applied to the adaptive control of engine air-fuel ratio based on a simulated engine model.
Besides, controller parameters were also analyzed, in which it is found that large hidden node number with small regularization parameter leads to the best performance.
A comparison among FOS-ELM and SGBP was also conducted.
The result indicates that FOS-ELM achieves better tracking and convergence performance than SGBP, since FOS-ELM tends to learn the unknown engine model globally whereas SGBP tends to “forget” what it has learnt.
This implies that FOS-ELM is more preferable for adaptive control applications.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wong, Pak-kin& Vong, Chi Man& Gao, Xiang Hui& Wong, Ka In. 2014. Adaptive Control Using Fully Online Sequential-Extreme Learning Machine and a Case Study on Engine Air-Fuel Ratio Regulation. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-457048
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wong, Pak-kin…[et al.]. Adaptive Control Using Fully Online Sequential-Extreme Learning Machine and a Case Study on Engine Air-Fuel Ratio Regulation. Mathematical Problems in Engineering No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-457048
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wong, Pak-kin& Vong, Chi Man& Gao, Xiang Hui& Wong, Ka In. Adaptive Control Using Fully Online Sequential-Extreme Learning Machine and a Case Study on Engine Air-Fuel Ratio Regulation. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-457048
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-457048
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
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