A NEW METHOD FOR EIGENSTRUCTURE EXTRACTION AND ITS NEURAL NETWORK IMPLEMENTATION

Yu Shuijun; Liang Diannong

Volume 18 Issue 2

Mar. 1996

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Yu Shuijun, Liang Diannong . A NEW METHOD FOR EIGENSTRUCTURE EXTRACTION AND ITS NEURAL NETWORK IMPLEMENTATION[J]. Journal of Electronics & Information Technology, 1996, 18(2): 121-126.

Citation:

Yu Shuijun, Liang Diannong . A NEW METHOD FOR EIGENSTRUCTURE EXTRACTION AND ITS NEURAL NETWORK IMPLEMENTATION[J]. Journal of Electronics & Information Technology, 1996, 18(2): 121-126.

Yu Shuijun, Liang Diannong . A NEW METHOD FOR EIGENSTRUCTURE EXTRACTION AND ITS NEURAL NETWORK IMPLEMENTATION[J]. Journal of Electronics & Information Technology, 1996, 18(2): 121-126.

Citation:

Yu Shuijun, Liang Diannong . A NEW METHOD FOR EIGENSTRUCTURE EXTRACTION AND ITS NEURAL NETWORK IMPLEMENTATION[J]. Journal of Electronics & Information Technology, 1996, 18(2): 121-126.

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A NEW METHOD FOR EIGENSTRUCTURE EXTRACTION AND ITS NEURAL NETWORK IMPLEMENTATION

Received Date: 1994-04-14
Rev Recd Date: 1995-08-29
Publish Date: 1996-03-19

Abstract

Abstract

The cost function for eigenstuctures extration is discussed in detail, one can obtain the largest eigenvector by minimizing the cost function. In order to obtain the other eigenvectors, a covariance matrix series is constructed. If one compares the cost function with the energy function of a neural network, the neural network can be introduced to extract the eigenvectors. Theoretical analysis and simulations show that the proposed method is reasonable and feasible.

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References(1)

References

Yang Bin. Subspace Tracking Based on the Projection Approach and the Recursive Least Squares Method. Minnesota: Proc. IV of ICASSP93, 1993, 145-148.[2]Mathew G, et al.Gauss-Newton Adaptive Subspace Estimation. California: Proc. IV of ICASSP92,[3]92, 205-208.[4]Luo FarLong, Li Yan-Da. Real-Time Neural Computation of the Noise Subspace for the MUSIC[5]Algorithm. Minnesota: Proc. I of ICASSP93, 1993, 485-487.[6]Kung San Yuan.[J].et al. A Neural Network Learning Algorithm for Adaptive Principal Component Extraction(APEX). New Mexico: Proc. of ICASSP9.1990,:-. A Neural Network Learning Algorithm for Adaptive Principal Component Extraction(APEX). New Mexico: Proc. of ICASSP' target='_blank'>[7]虞水俊,等.国防科技大学学报,1995, 17(1): 16-21.[8]虞水俊,孔铁生,梁甸农.电子学报,1995, 23(8): 109-111.

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