Theory and Numerical Analysis of Extreme Learning Machine and Its Application for Different Degrees of Defect Recognition of Hoisting Wire Rope
المؤلفون المشاركون
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-08-29
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
An improved classification approach is proposed to solve the hot research problem of some complex multiclassification samples based on extreme learning machine (ELM).
ELM was proposed based on the single-hidden layer feed-forward neural network (SLFNN).
ELM is characterized by the easier parameter selection rules, the faster converge speed, the less human intervention, and so on.
In order to further improve the classification precision of ELM, an improved generation method of the network structure of ELM is developed by dynamically adjusting the number of hidden nodes.
The number change of the hidden nodes can serve as the computational updated step length of the ELM algorithm.
In this paper, the improved algorithm can be called the variable step incremental extreme learning machine (VSI-ELM).
In order to verify the effect of the hidden layer nodes on the performance of ELM, an open-source machine learning database (University of California, Irvine (UCI)) is provided by the performance test data sets.
The regression and classification experiments are used to study the performance of the VSI-ELM model, respectively.
The experimental results show that the VSI-ELM algorithm is valid.
The classification of different degrees of broken wires is now still a problem in the nondestructive testing of hoisting wire rope.
The magnetic flux leakage (MFL) method of wire rope is an efficient nondestructive method which plays an important role in safety evaluation.
Identifying the proposed VSI-ELM model is effective and reliable for actually applying data, and it is used to identify the classification problem of different types of samples from MFL signals.
The final experimental results show that the VSI-ELM algorithm is of faster classification speed and higher classification accuracy of different broken wires.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zhao, Zhike& Zhang, Xiaoguang. 2018. Theory and Numerical Analysis of Extreme Learning Machine and Its Application for Different Degrees of Defect Recognition of Hoisting Wire Rope. Shock and Vibration،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1215235
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zhao, Zhike& Zhang, Xiaoguang. Theory and Numerical Analysis of Extreme Learning Machine and Its Application for Different Degrees of Defect Recognition of Hoisting Wire Rope. Shock and Vibration No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1215235
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zhao, Zhike& Zhang, Xiaoguang. Theory and Numerical Analysis of Extreme Learning Machine and Its Application for Different Degrees of Defect Recognition of Hoisting Wire Rope. Shock and Vibration. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1215235
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1215235
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