一種廣義主成分提取算法及其收斂性分析
doi: 10.11999/JEIT151433
基金項目:
國家自然科學基金面上項目(61074072, 61374120)，國家杰出青年基金(61025014)
A Generalized Principal Component Extraction Algorithm and Its Convergence Analysis
Funds:
The National Natural Science Foundation of China (61074072, 61374120), The National Science Fund for Distinguished Youth Scholars (61025014)
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摘要: 廣義主成分分析在現(xiàn)代信號處理的諸多領(lǐng)域發(fā)揮著重要的作用。目前，自適應(yīng)廣義主成分分析算法還并不多見。針對這一現(xiàn)狀，該文提出一種快速收斂的廣義主成分分析算法，并通過理論分析所提算法的確定性離散時間系統(tǒng)，導出了保證算法收斂的學習因子和初始權(quán)向量模值等邊界條件。仿真實驗和實際應(yīng)用驗證了所提算法的正確性和有用性。仿真結(jié)果還表明，所提算法比現(xiàn)有同類算法具有更快的收斂速度和更高的估計精度。
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關(guān)鍵詞:
- 廣義主成分 /
- 確定性離散時間 /
- 收斂性分析 /
- 神經(jīng)網(wǎng)絡(luò)
Abstract: The generalized principal component analysis plays an important roles in many fields of modern signal processing. However, up to now, there are few algorithms, which can extract the generalized principal component adaptively. In this paper, a generalized principal component extraction algorithm, which has fast convergence speed, is proposed. The corresponding Deterministic Discrete Time (DDT) system of the proposed algorithm is analyzed and some conditions about the learning rate and initial weight vector are also obtained. Finally, computer simulation and practical application results show that compared with some existing algorithms, the proposed algorithm has faster convergence speed and higher estimation accuracy. -
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