کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
409175 | 679057 | 2008 | 5 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A neural networks learning algorithm for minor component analysis and its convergence analysis
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
The eigenvector associated with the smallest eigenvalue of the autocorrelation matrix of input signals is called minor component. Minor component analysis (MCA) is a statistical approach for extracting minor component from input signals and has been applied in many fields of signal processing and data analysis. In this letter, we propose a neural networks learning algorithm for estimating adaptively minor component from input signals. Dynamics of the proposed algorithm are analyzed via a deterministic discrete time (DDT) method. Some sufficient conditions are obtained to guarantee convergence of the proposed algorithm.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 71, Issues 7–9, March 2008, Pages 1748–1752
Journal: Neurocomputing - Volume 71, Issues 7–9, March 2008, Pages 1748–1752
نویسندگان
Dezhong Peng, Zhang Yi, JianCheng Lv, Yong Xiang,