一种新的特征结构提取方法及其神经网络实现
A NEW METHOD FOR EIGENSTRUCTURE EXTRACTION AND ITS NEURAL NETWORK IMPLEMENTATION
-
摘要: 本文首先建立了特征结构问题的代价函数表示,通过对代价函数求极小可以求得原始数据协方差矩阵的最大特征向量。为了求得其他特征向量,特构造了一个协方差矩阵序列。为实现对代价函数求极小,可把高阶神经网络引入特征结构提取中。这种方法比较直观,它将网络稳定时的输出与所求协方差矩阵的主特征向量的各个分量相对应。理论分析和计算机仿真均验证了这种方法的正确性。
-
关键词:
- 特征结构; 代价函数; 神经网络
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. -
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. -
计量
- 文章访问数: 2271
- HTML全文浏览量: 172
- PDF下载量: 565
- 被引次数: 0
下载:
