title: Fuzzy auto-associative neural networks for principal component extraction of noisy data.
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.
authors.affiliation: Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan, R.O.C. tnyang@hpc.ee.ntu.edu.tw
authors.forename: T N
authors.initials: TN
authors.lastname: Yang
authors.forename: S D
authors.initials: SD
authors.lastname: Wang