Title :
Fuzzy auto-associative neural networks for principal component extraction of noisy data
Author :
Yang, Tai-Ning ; Wang, Sheng-De
Author_Institution :
Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan
fDate :
5/1/2000 12:00:00 AM
Abstract :
In this paper, we propose a fuzzy auto-associative neural network for principal component extraction. The objective function is based on reconstructing the inputs from the corresponding outputs of the auto-associative neural network. Unlike the traditional approaches, the proposed criterion is a fuzzy mean squared error. We prove that the proposed objective function is an appropriate fuzzy formulation of auto-associative neural network for principal component extraction. Simulations are given to show the performances of the proposed neural networks in comparison with the existing method
Keywords :
content-addressable storage; fuzzy neural nets; mean square error methods; noise; pattern clustering; principal component analysis; PCA; fuzzy auto-associative neural network; fuzzy mean squared error; noisy data; objective function; principal component extraction; Computational modeling; Computer networks; Data mining; Eigenvalues and eigenfunctions; Equations; Fuzzy neural networks; Neural networks; Neurons; Principal component analysis; Robustness;
Journal_Title :
Neural Networks, IEEE Transactions on
