کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4948498 | 1439613 | 2016 | 20 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Recursive reduced kernel based extreme learning machine for aero-engine fault pattern recognition
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Kernel based extreme learning machine (K-ELM) has better generalization performance than basic ELM with less tuned parameters in most applications. However the original K-ELM is lack of sparseness, which makes the model scale grows linearly with sample size. This paper focuses on sparsity of K-ELM and proposes recursive reduced kernel based extreme learning machine (RR-KELM). The proposed algorithm chooses samples making more contribution to target function to constitute kernel dictionary meanwhile considering all the constraints generated by the whole training set. As a result it can simplify model structure and realize sparseness of K-ELM. Experimental results on benchmark datasets show that no matter for regression or classification problems, RR-KELM produces more compact model structure and higher real-time in comparison with other methods. The application of RR-KELM for aero-engine fault pattern recognition is also given in this paper. The simulation results demonstrate that RR-KELM has a high recognition rate on aero-engine fault pattern based on measurable parameters of aero-engine.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 214, 19 November 2016, Pages 1038-1045
Journal: Neurocomputing - Volume 214, 19 November 2016, Pages 1038-1045
نویسندگان
Cheng-Xin You, Jin-Quan Huang, Feng Lu,